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Xiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar, and Chandra M. Sehgal. "The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images." Ultrasound 25, no. 2 (January 25, 2017): 98–106. http://dx.doi.org/10.1177/1742271x17690425.
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Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area ( Oa) between the margins, and area under the ROC curves ( Az). Results The lesion size from leak-plugging segmentation correlated closely with that from manual tracing ( R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. Conclusion The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.
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Barteček, R., N. E. M. van Haren, P. C. M. P. Koolschijn, H. E. Hulshoff Pol, and R. S. Kahn. "Comparison of manual and automatic methods of hippocampus segmentation." European Psychiatry 26, S2 (March 2011): 914. http://dx.doi.org/10.1016/s0924-9338(11)72619-0.
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IntroductionPsychiatric Patients show abnormalities in volumes of several subcortical structures. Recently wider usage of automated segmentation methods in research of these abnormalities based on MR images has become possible. However manual segmentation is still considered to be the gold standard.ObjectivesTo compare differences in hippocampus volumes between manual segmentation and 2 packages for automatic segmentation (FSL and FreeSurfer).AimTo explore the overlap and differences between different segmentation methods used for segmentation of subcortical structures.MethodsStructural MR brain scans were aquired from 98 subjects (53 schizophrenia patients, 45 controls). Volumes of left and right hippocampus were measured after manual, FreeSurfer and FSL segmentations. Differences between volumes from different methods were tested by the t-test (using R). In addition percent volume differences, Pearson correlations, Bland-Altman plots and Cronbach’s alpha were computed.ResultsBoth automatic methods yielded significantly larger hippocampal volumes than the manual segmentation. FreeSurfer volumes showed a higher correlation and lower percent volume difference with manual segmentation than FSL. Bland-Altman plots and Cronbach’s alpha showed only limited agreement between manual and both automatic methods.ConclusionsAlthough volumes acquired by FreeSurfer appeared to be more related to manual segmentation, clear superiority of either of automatic methods could not be demonstrated. Therefore, all three methods seem to measure other aspects of hippocampus volume. An useful approach would be to compare effect-size of the difference between patients and healthy controls using different segmentation methods. We are currently pursuing this in a larger sample.
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Dionisio, Fernando Carrasco Ferreira, Larissa Santos Oliveira, Mateus de Andrade Hernandes, Edgard Eduard Engel, Paulo Mazzoncini de Azevedo-Marques, and Marcello Henrique Nogueira-Barbosa. "Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times." Radiologia Brasileira 54, no. 3 (June 2021): 155–64. http://dx.doi.org/10.1590/0100-3984.2020.0028.
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Abstract Objective: To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). Materials and Methods: This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). Results: In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). Conclusion: There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.
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Kemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, Akshay Chaudhari, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, and Ender Konukoglu. "Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 4 (December 23, 2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.
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Abstract Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (− 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (− 5.6 ± 7.6%, p < 0.001, effect size: 0.73). Discussion Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.
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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Segmentation of design protocol using EEG." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 1 (April 3, 2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.
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AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.
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Nishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, and Hiroshi Yamada. "Deep generative models for automated muscle segmentation in computed tomography scanning." PLOS ONE 16, no. 9 (September 10, 2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.
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Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.
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Outif, A., and M. Mosely. "1274 poster MANUAL SEGMENTATION (HOW ACCURATE ARE WE?) (ANALYSE OF MANUAL SEGMENTATION ERROR)." Radiotherapy and Oncology 99 (May 2011): S475. http://dx.doi.org/10.1016/s0167-8140(11)71396-2.
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Bowes, Michael Antony, Gwenael Alain Guillard, Graham Richard Vincent, Alan Donald Brett, Christopher Brian Hartley Wolstenholme, and Philip Gerard Conaghan. "Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative." Journal of Rheumatology 47, no. 2 (April 15, 2019): 282–89. http://dx.doi.org/10.3899/jrheum.180541.
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Objective.Accurate automated segmentation of cartilage should provide rapid reliable outcomes for both epidemiological studies and clinical trials. We aimed to assess the precision and responsiveness of cartilage thickness measured with careful manual segmentation or a novel automated technique.Methods.Agreement of automated segmentation was assessed against 2 manual segmentation datasets: 379 magnetic resonance images manually segmented in-house (training set), and 582 from the Osteoarthritis Initiative with data available at 0, 1, and 2 years (biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, and change with pairwise Student t test in the central medial femur (cMF) and tibia regions (cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardized response means (SRM).Results.Agreement of manual versus automated methods was excellent with no meaningful systematic bias (training set: cMF bias 0.1 mm, 95% CI ± 0.35; biomarkers set: bias 0.1 mm ± 0.4). The smallest detectable difference for cMF was 0.13 mm (coefficient of variation 3.1%), and for cMT 0.16 mm(2.65%). Reported change using manual segmentations in the cMF region at 1 year was −0.031 mm (95% CI −0.022, −0.039), p < 10−4, SRM −0.31 (−0.23, −0.38); and at 2 years was −0.071 (−0.058, −0.085), p < 10−4, SRM −0.43 (−0.36, −0.49). Reported change using automated segmentations in the cMF at 1 year was −0.059 (−0.047, −0.071), p < 10−4, SRM −0.41 (−0.34, −0.48); and at 2 years was −0.14 (−0.123, −0.157, p < 10−4, SRM −0.67 (−0.6, −0.72).Conclusion.A novel cartilage segmentation method provides highly accurate and repeatable measures with cartilage thickness measurements comparable to those of careful manual segmentation, but with improved responsiveness.
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Clark, A. E., B. Biffi, R. Sivera, A. Dall'Asta, L. Fessey, T. L. Wong, G. Paramasivam, D. Dunaway, S. Schievano, and C. C. Lees. "Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images." Royal Society Open Science 7, no. 11 (November 2020): 201342. http://dx.doi.org/10.1098/rsos.201342.
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Fetal craniofacial abnormalities are challenging to detect and diagnose on prenatal ultrasound (US). Image segmentation and computer analysis of three-dimensional US volumes of the fetal face may provide an objective measure to quantify fetal facial features and identify abnormalities. We have developed and tested an atlas-based partially automated facial segmentation algorithm; however, the volumes require additional manual segmentation (MS), which is time and labour intensive and may preclude this method from clinical adoption. These manually refined segmentations can then be used as a reference (atlas) by the partially automated segmentation algorithm to improve algorithmic performance with the aim of eliminating the need for manual refinement and developing a fully automated system. This study assesses the inter- and intra-operator variability of MS and tests an optimized version of our automatic segmentation (AS) algorithm. The manual refinements of 15 fetal faces performed by three operators and repeated by one operator were assessed by Dice score, average symmetrical surface distance and volume difference. The performance of the partially automatic algorithm with difference size atlases was evaluated by Dice score and computational time. Assessment of the manual refinements showed low inter- and intra-operator variability demonstrating its suitability for optimizing the AS algorithm. The algorithm showed improved performance following an increase in the atlas size in turn reducing the need for manual refinement.
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Andreassen, Maren Marie Sjaastad, Pål Erik Goa, Torill Eidhammer Sjøbakk, Roja Hedayati, Hans Petter Eikesdal, Callie Deng, Agnes Østlie, Steinar Lundgren, Tone Frost Bathen, and Neil Peter Jerome. "Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 2 (September 27, 2019): 317–28. http://dx.doi.org/10.1007/s10334-019-00778-8.
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Abstract Objectives To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. Materials and methods Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. Results No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively. Discussion Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.
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Tuncay, V., N. Prakken, P. M. A. van Ooijen, R. P. J. Budde, T. Leiner, and M. Oudkerk. "Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/648283.
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Objective. The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets.Methods. The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded.Results. Mean differences from TTE were −0.19 (95% CI: −0.74 to 0.34) cm2for manual and −0.10 (95% CI: −0.45 to 0.25) cm2for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.Conclusion. Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.
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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Sverre Langørgen, Helena Bertilsson, Tone F. Bathen, and Mattijs Elschot. "The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images." Diagnostics 11, no. 9 (September 16, 2021): 1690. http://dx.doi.org/10.3390/diagnostics11091690.
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Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.
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Schmidt-Richberg, A., J. Fiehler, T. Illies, D. Möller, H. Handels, D. Säring, and N. D. Forkert. "Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets." Methods of Information in Medicine 51, no. 05 (2012): 415–22. http://dx.doi.org/10.3414/me11-02-0037.
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Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.
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Yazdi, Mahsa Badiee, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (August 2016): 1650025. http://dx.doi.org/10.4015/s1016237216500253.
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Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify image segmentation methods. In this paper, we propose some methods to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in three ways, in order to improve segmentation of brain MR images with high noise level and spatial complexity. In real images, we had a considerable improvement in similarity index of three classes (white matter, gray matter, CSF) and in simulated images with different noise levels evaluation criteria of white matter and gray matter has improved.
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Dury, Richard, Rob Dineen, Anbarasu Lourdusamy, and Richard Grundy. "Semi-automated medulloblastoma segmentation and influence of molecular subgroup on segmentation quality." Neuro-Oncology 21, Supplement_4 (October 2019): iv14. http://dx.doi.org/10.1093/neuonc/noz167.060.
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Abstract Medulloblastoma is the most common malignant brain tumour in children. Segmenting the tumour itself from the surrounding tissue on MRI scans has shown to be useful for neuro-surgical planning, by allowing a better understanding of the tumour margin with 3D visualisation. However, manual segmentation of medulloblastoma is time consuming, prone to bias and inter-observer discrepancies. Here we propose a semi-automatic patient based segmentation pipeline with little sensitivity to tumour location and minimal user input. Using SPM12 “Segment” as a base, an additional tissue component describing the medulloblastoma is included in the algorithm. The user is required to define the centre of mass and a single surface point of the tumour, creating an approximate enclosing sphere. The calculated volume is confined to the cerebellum to minimise misclassification of other intracranial structures. This process typically takes 5 minutes from start to finish. This method was applied to 97 T2-weighted scans of paediatric medulloblastoma (7 WNT, 6 SHH, 17 Gr3, 26 Gr4, 41 unknown subtype); resulting segmented volumes were compared to manual segmentations. An average Dice coefficient of 0.85±0.07 was found, with the Group 4 subtype demonstrating a significantly higher similarity with manual segmentation than other subgroups (0.88±0.04). When visually assessing the 10 cases with the lowest Dice coefficients, it was found that the misclassification of oedema was the most common source of error. As this method is independent of image contrast, segmentation could be improved by applying it to images that are less sensitive to oedema, such as T1.
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Desser, Dmitriy, Francisca Assunção, Xiaoguang Yan, Victor Alves, Henrique M. Fernandes, and Thomas Hummel. "Automatic Segmentation of the Olfactory Bulb." Brain Sciences 11, no. 9 (August 28, 2021): 1141. http://dx.doi.org/10.3390/brainsci11091141.
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The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject’s smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater’s segmentation and 5.92 ± 0.81 for the second rater’s manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3–5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.
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Xue, Jie, Bao Wang, Yang Ming, Xuejun Liu, Zekun Jiang, Chengwei Wang, Xiyu Liu, et al. "Deep learning–based detection and segmentation-assisted management of brain metastases." Neuro-Oncology 22, no. 4 (December 23, 2019): 505–14. http://dx.doi.org/10.1093/neuonc/noz234.
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Abstract Background Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning–based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. Methods The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity, and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t-tests and analysis of variance were employed for statistical analysis. Results The BMDS net can detect all BM, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross-validation of the dataset with 1201 patients: the sensitivity was 0.96 ± 0.03 (range, 0.84–0.99), the specificity was 0.99 ± 0.0002 (range, 0.99–1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62–0.95) for total tumor volume. Similar performances on the other 2 datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BM in various settings. Conclusions The BMDS net yields accurate detection and segmentation of BM automatically and could assist stereotactic radiotherapy management for diagnosis, therapy planning, and follow-up.
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Cook, Daniel J., David A. Gladowski, Heather E. Acuff, Matthew S. Yeager, and Boyle C. Cheng. "Variability of manual lumbar spine segmentation." International Journal of Spine Surgery 6, no. 1 (December 2012): 167–73. http://dx.doi.org/10.1016/j.ijsp.2012.04.002.
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Tam, Lydia, Edward Lee, Michelle Han, Jason Wright, Leo Chen, Jenn Quon, Robert Lober, et al. "IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii359. http://dx.doi.org/10.1093/neuonc/noaa222.357.
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Abstract BACKGROUND Brain tumors are the most common solid malignancies in childhood, many of which develop in the posterior fossa (PF). Manual tumor measurements are frequently required to optimize registration into surgical navigation systems or for surveillance of nonresectable tumors after therapy. With recent advances in artificial intelligence (AI), automated MRI-based tumor segmentation is now feasible without requiring manual measurements. Our goal was to create a deep learning model for automated PF tumor segmentation that can register into navigation systems and provide volume output. METHODS 720 pre-surgical MRI scans from five pediatric centers were divided into training, validation, and testing datasets. The study cohort comprised of four PF tumor types: medulloblastoma, diffuse midline glioma, ependymoma, and brainstem or cerebellar pilocytic astrocytoma. Manual segmentation of the tumors by an attending neuroradiologist served as “ground truth” labels for model training and evaluation. We used 2D Unet, an encoder-decoder convolutional neural network architecture, with a pre-trained ResNet50 encoder. We assessed ventricle segmentation accuracy on a held-out test set using Dice similarity coefficient (0–1) and compared ventricular volume calculation between manual and model-derived segmentations using linear regression. RESULTS Compared to the ground truth expert human segmentation, overall Dice score for model performance accuracy was 0.83 for automatic delineation of the 4 tumor types. CONCLUSIONS In this multi-institutional study, we present a deep learning algorithm that automatically delineates PF tumors and outputs volumetric information. Our results demonstrate applied AI that is clinically applicable, potentially augmenting radiologists, neuro-oncologists, and neurosurgeons for tumor evaluation, surveillance, and surgical planning.
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Stegmaier, Johannes, Nico Peter, Julia Portl, Ira V. Mang, Rasmus Schröder, Heike Leitte, Ralf Mikut, and Markus Reischl. "A framework for feedback-based segmentation of 3D image stacks." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 437–41. http://dx.doi.org/10.1515/cdbme-2016-0097.
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Abstract3D segmentation has become a widely used technique. However, automatic segmentation does not deliver high accuracy in optically dense images and manual segmentation lowers the throughput drastically. Therefore, we present a workflow for 3D segmentation being able to forecast segments based on a user-given ground truth. We provide the possibility to correct wrong forecasts and to repeatedly insert ground truth in the process. Our aim is to combine automated and manual segmentation and therefore to improve accuracy by a tunable amount of manual input.
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Löffler, Katharina, Tim Scherr, and Ralf Mikut. "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction." PLOS ONE 16, no. 9 (September 7, 2021): e0249257. http://dx.doi.org/10.1371/journal.pone.0249257.
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Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
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Tran, Carol, Orit Glenn, Christopher Hess, and Andreas Rauschecker. "4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network." Journal of Clinical and Translational Science 4, s1 (June 2020): 45–46. http://dx.doi.org/10.1017/cts.2020.169.
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OBJECTIVES/GOALS: We seek to develop an automated deep learning-based method for segmentation and volumetric quantification of the fetal brain on T2-weighted fetal MRIs. We will evaluate the performance of the algorithm by comparing it to gold standard manual segmentations. The method will be used to create a normative sample of brain volumes across gestational ages. METHODS/STUDY POPULATION: We will adapt a U-Net convolutional neural network architecture for fetal brain MRIs using 3D volumes. After re-sampling 2D fetal brain acquisitions to 3mm3 3D volumes using linear interpolation, the network will be trained to perform automated brain segmentation on 40 randomly-sampled, normal fetal brain MRI scans of singleton pregnancies. Training will be performed in 3 acquisition planes (axial, coronal, sagittal). Performance will be evaluated on 10 test MRIs (in 3 acquisition planes, 30 total test samples) using Dice scores, compared to radiologists’ manual segmentations. The algorithm’s performance on measuring total brain volume will also be evaluated. RESULTS/ANTICIPATED RESULTS: Based on the success of prior U-net architectures for volumetric segmentation tasks in medical imaging (e.g. Duong et al., 2019), we anticipate that the convolutional neural network will accurately provide segmentations and associated volumetry of fetal brains in fractions of a second. We anticipate median Dice scores greater than 0.8 across our test sample. Once validated, the method will retrospectively generate a normative database of over 1500 fetal brain volumes across gestational ages (18 weeks to 30 weeks) collected at our institution. DISCUSSION/SIGNIFICANCE OF IMPACT: Quantitative estimates of brain volume, and deviations from normative data, would be a major advancement in objective clinical assessments of fetal MRI. Such data can currently only be obtained through laborious manual segmentations; automated deep learning methods have the potential to reduce the time and cost of this process.
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Song, Young Ju, Hyo Sung Kwak, Gyung Ho Chung, and Seongil Jo. "Quantification of Carotid Intraplaque Hemorrhage: Comparison between Manual Segmentation and Semi-Automatic Segmentation on Magnetization-Prepared Rapid Acquisition with Gradient-Echo Sequences." Diagnostics 9, no. 4 (November 11, 2019): 184. http://dx.doi.org/10.3390/diagnostics9040184.
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Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.
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Pien, Homer H., Mukund Desai, and Jayant Shah. "Segmentation of MR Images using Curve Evolution and Prior Information." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 08 (December 1997): 1233–45. http://dx.doi.org/10.1142/s0218001497000573.
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Segmentation of anatomic structures of the human brain from MR images is important for assessing treatment efficacy, screening for anomalies, and improving our understanding of human development. The labor intensive nature of manual segmentation, however, makes such a technique viable only in selected cases. In this paper we present a new approach to segmentation that involves only minimal human interactions. The technique utilizes a variational formulation to obtain an edge-strength function over the region of interest, and uses curve evolution and a pre-segmented atlas to guide the actual segmentation process. The approach is demonstrated via both phantoms and actual MR images, and when applied to the lateral ventricles and caudate nucleus, showed a size accuracy error of 5%–20% with respect to manual segmentation, depending on the manual segmentation method utilized.
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Ahmed, Arwa, and Alnazeer Osman. "Optic Disc Segmentation Using Manual Thresholding Technique." Journal of Clinical Engineering 44, no. 1 (2019): 28–34. http://dx.doi.org/10.1097/jce.0000000000000295.
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Jaware, Tushar H., K. B. Khanchandani, and Anita Zurani. "An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI." American Journal of Perinatology 36, no. 11 (December 15, 2018): 1157–70. http://dx.doi.org/10.1055/s-0038-1675375.
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Background Segmentation of brain MR images of neonates is a primary step for assessment of brain evolvement. Advanced segmentation techniques used for adult brain MRI are not companionable for neonates, due to extensive dissimilarities in tissue properties and head structure. Existing segmentation methods for neonates utilizes brain atlases or requires manual elucidation, which results into improper and atlas dependent segmentation. Objective The primary objective of this work is to develop fully automatic, atlas free, and robust system to segment and classify brain tissues of newborn infants from magnetic resonance images. Study Design In this study, we propose a fully automatic, atlas-free pipeline based Neural Tree approach for segmentation of newborn brain MRI which utilizes resourceful local resemblance factor such as concerning, connectivity, structure, and relative tissue location. Physical collaboration and uses of an atlas are not required in proposed method and at the same time skirting atlas-associated bias which results in improved segmentation. Proposed technique segments and classify brain tissues both at global and tissue level. Results We examined our results through visual assessment by neonatologists and quantitative comparisons that show first-rate concurrence with proficient manual segmentations. The implementation results of the proposed technique provided a good overall accuracy of 91.82% for the segmentation of brain tissues as compared with other methods. Conclusion The pipelined-based neural tree approach along with local similarity factor segments and classify brain tissues. The proposed automated system have higher dice similarity coefficient as well as computational speed.
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Lo Giudice, Antonino, Vincenzo Ronsivalle, Cristina Grippaudo, Alessandra Lucchese, Simone Muraglie, Manuel O. Lagravère, and Gaetano Isola. "One Step before 3D Printing—Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandible: A Comparative Study Using a Surface-to-Surface Matching Technique." Materials 13, no. 12 (June 21, 2020): 2798. http://dx.doi.org/10.3390/ma13122798.
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The accuracy of 3D reconstructions of the craniomaxillofacial region using cone beam computed tomography (CBCT) is important for the morphological evaluation of specific anatomical structures. Moreover, an accurate segmentation process is fundamental for the physical reconstruction of the anatomy (3D printing) when a preliminary simulation of the therapy is required. In this regard, the objective of this study is to evaluate the accuracy of four different types of software for the semiautomatic segmentation of the mandibular jaw compared to manual segmentation, used as a gold standard. Twenty cone beam computed tomography (CBCT) with a manual approach (Mimics) and a semi-automatic approach (Invesalius, ITK-Snap, Dolphin 3D, Slicer 3D) were selected for the segmentation of the mandible in the present study. The accuracy of semi-automatic segmentation was evaluated: (1) by comparing the mandibular volumes obtained with semi-automatic 3D rendering and manual segmentation and (2) by deviation analysis between the two mandibular models. An analysis of variance (ANOVA) was used to evaluate differences in mandibular volumetric recordings and for a deviation analysis among the different software types used. Linear regression was also performed between manual and semi-automatic methods. No significant differences were found in the total volumes among the obtained 3D mandibular models (Mimics = 40.85 cm3, ITK-Snap = 40.81 cm3, Invesalius = 40.04 cm3, Dolphin 3D = 42.03 cm3, Slicer 3D = 40.58 cm3). High correlations were found between the semi-automatic segmentation and manual segmentation approach, with R coefficients ranging from 0,960 to 0,992. According to the deviation analysis, the mandibular models obtained with ITK-Snap showed the highest matching percentage (Tolerance A = 88.44%, Tolerance B = 97.30%), while those obtained with Dolphin 3D showed the lowest matching percentage (Tolerance A = 60.01%, Tolerance B = 87.76%) (p < 0.05). Colour-coded maps showed that the area of greatest mismatch between semi-automatic and manual segmentation was the condylar region and the region proximate to the dental roots. Despite the fact that the semi-automatic segmentation of the mandible showed, in general, high reliability and high correlation with the manual segmentation, caution should be taken when evaluating the morphological and dimensional characteristics of the condyles either on CBCT-derived digital models or physical models (3D printing).
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Dunmore, Christopher J., Gert Wollny, and Matthew M. Skinner. "MIA-Clustering: a novel method for segmentation of paleontological material." PeerJ 6 (February 23, 2018): e4374. http://dx.doi.org/10.7717/peerj.4374.
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Paleontological research increasingly uses high-resolution micro-computed tomography (μCT) to study the inner architecture of modern and fossil bone material to answer important questions regarding vertebrate evolution. This non-destructive method allows for the measurement of otherwise inaccessible morphology. Digital measurement is predicated on the accurate segmentation of modern or fossilized bone from other structures imaged in μCT scans, as errors in segmentation can result in inaccurate calculations of structural parameters. Several approaches to image segmentation have been proposed with varying degrees of automation, ranging from completely manual segmentation, to the selection of input parameters required for computational algorithms. Many of these segmentation algorithms provide speed and reproducibility at the cost of flexibility that manual segmentation provides. In particular, the segmentation of modern and fossil bone in the presence of materials such as desiccated soft tissue, soil matrix or precipitated crystalline material can be difficult. Here we present a free open-source segmentation algorithm application capable of segmenting modern and fossil bone, which also reduces subjective user decisions to a minimum. We compare the effectiveness of this algorithm with another leading method by using both to measure the parameters of a known dimension reference object, as well as to segment an example problematic fossil scan. The results demonstrate that the medical image analysis-clustering method produces accurate segmentations and offers more flexibility than those of equivalent precision. Its free availability, flexibility to deal with non-bone inclusions and limited need for user input give it broad applicability in anthropological, anatomical, and paleontological contexts.
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Dabir, Supriya, Vaidehi Bhatt, Deepak Bhatt, Mohan Rajan, Preetam Samant, Sivakumar Munusamy, C. A. B. Webers, and T. T. J. M. Berendschot. "Need for manual segmentation in optical coherence tomography angiography of neovascular age-related macular degeneration." PLOS ONE 15, no. 12 (December 31, 2020): e0244828. http://dx.doi.org/10.1371/journal.pone.0244828.
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Purpose To compare the characteristics of eyes that had manual vs. automated segmentation of choroidal neovascular membrane (CNVM) using optical coherence tomography angiography (OCTA). Methods All patients with CNVM underwent OCTA using the Zeiss Angioplex Cirrus 5000. Slabs of the avascular outer retina, outer retina to choriocapillaris (ORCC) region and choriocapillaris were generated. Manual segmentation was done when there were significant segmentation artifacts. Presence of activity of CNVM was adjudged by the presence of subretinal fluid (SRF) on structural OCT and was compared to activity detected on en face OCTA slabs based on well-defined criteria. Results Eighty-one eyes of 81 patients were recruited of which manual segmentation was required in 46 (57%). Eyes with automated segmentation had significantly more CNVM in the ORCC (75%) whereas those with manual segmentation had deeper CNVM (sub-RPE = 22%, intra-PED = 22%) (p<0.001). Twenty eyes (25%) were found to have active CNVM on both the structural OCT and OCTA while an additional 19 eyes were presumed to have active CNVM on OCTA alone. There was only modest concordance between disease activity detected using structural OCT and OCTA (Kappa = 0.47, 95% CI = 0.30 to 0.64). Conclusions Manual segmentation of OCTA is required in more than 50% eyes with CNVM and this progressively increases with increasing depth of CNVM location from the ORCC to below the RPE. There is moderate concordance between OCTA and structural OCT in determining CNVM activity.
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Arafati, Arghavan, Daisuke Morisawa, Michael R. Avendi, M. Reza Amini, Ramin A. Assadi, Hamid Jafarkhani, and Arash Kheradvar. "Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks." Journal of The Royal Society Interface 17, no. 169 (August 2020): 20200267. http://dx.doi.org/10.1098/rsif.2020.0267.
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A major issue in translation of the artificial intelligence platforms for automatic segmentation of echocardiograms to clinics is their generalizability. The present study introduces and verifies a novel generalizable and efficient fully automatic multi-label segmentation method for four-chamber view echocardiograms based on deep fully convolutional networks (FCNs) and adversarial training. For the first time, we used generative adversarial networks for pixel classification training, a novel method in machine learning not currently used for cardiac imaging, to overcome the generalization problem. The method's performance was validated against manual segmentations as the ground-truth. Furthermore, to verify our method's generalizability in comparison with other existing techniques, we compared our method's performance with a state-of-the-art method on our dataset in addition to an independent dataset of 450 patients from the CAMUS (cardiac acquisitions for multi-structure ultrasound segmentation) challenge. On our test dataset, automatic segmentation of all four chambers achieved a dice metric of 92.1%, 86.3%, 89.6% and 91.4% for LV, RV, LA and RA, respectively. LV volumes' correlation between automatic and manual segmentation were 0.94 and 0.93 for end-diastolic volume and end-systolic volume, respectively. Excellent agreement with chambers’ reference contours and significant improvement over previous FCN-based methods suggest that generative adversarial networks for pixel classification training can effectively design generalizable fully automatic FCN-based networks for four-chamber segmentation of echocardiograms even with limited number of training data.
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Haniff, Nurin Syazwina Mohd, Muhammad Khalis Abdul Karim, Nurul Huda Osman, M. Iqbal Saripan, Iza Nurzawani Che Isa, and Mohammad Johari Ibahim. "Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)." Diagnostics 11, no. 9 (August 30, 2021): 1573. http://dx.doi.org/10.3390/diagnostics11091573.
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Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.
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Aljondi, Rowa, Cassandra Szoeke, Chris Steward, Elaine Lui, Salem Alghamdi, and Patricia Desmond. "The impact of hippocampal segmentation methods on correlations with clinical data." Acta Radiologica 61, no. 7 (November 12, 2019): 953–63. http://dx.doi.org/10.1177/0284185119885120.
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Background In vivo measurement of hippocampal volume with magnetic resonance imaging (MRI) has become an important element in neuroimaging research. However, hippocampal volumetric findings and their relationship with cardiovascular risk factors and memory performance are still controversial and inconsistent for non-demented adults. Purpose To compare total and regional hippocampal volumes from manual tracing and automated Freesurfer segmentation methods and their relationship with mid-life clinical data and late-life verbal episodic memory performance in older women. Material and Methods This study used structural MRI datasets from 161 women who were scanned in 2012 and underwent neuropsychological assessments. Of these participants, 135 women had completed baseline measures of cardiovascular risk factors in 1992. Results Our results showed a significant correlation between manual tracing and automated Freesurfer output segmentations of total (r = 0.71), anterior (r = 0.65), and posterior (r = 0.38) hippocampal volumes. Mid-life Framingham Cardiovascular Risk Profile score is not associated with late-life hippocampal volumes, adjusted for intracranial volume, age, education, and apolipoprotein E gene ε4 status. Anterior hippocampal volume segmented either with manual tracing or automated Freesurfer software is sensitive to changes in mid-life high-density lipoprotein (HDL) cholesterol level, while posterior hippocampal volume is linked with verbal episodic memory performance in elderly women. Conclusion These findings support the use of Freesurfer automated segmentation measures for large datasets as being highly correlated with the manual tracing method. In addition, our results suggest intervention strategies that target mid-life HDL cholesterol level in women.
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Lee, Aaron Y., Cecilia S. Lee, Pearse A. Keane, and Adnan Tufail. "Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation." Journal of Ophthalmology 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6571547.
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Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework.Methods.A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to$0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework.Results.More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson’s correlation of interrater reliability was 0.995 (p<0.0001) and coefficient of determination was 0.991. The cost of segmenting the macular volume was$1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes.Conclusions.Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images.
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Montgomery, Mary Katherine, John David, Haikuo Zhang, Sripad Ram, Shibing Deng, Vidya Premkumar, Lisa Manzuk, Ziyue Karen Jiang, and Anand Giddabasappa. "Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography." PLOS ONE 16, no. 6 (June 17, 2021): e0252950. http://dx.doi.org/10.1371/journal.pone.0252950.
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Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.
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Sensakovic, William F., Adam Starkey, Rachael Roberts, Christopher Straus, Philip Caligiuri, Masha Kocherginsky, and Samuel G. Armato. "The influence of initial outlines on manual segmentation." Medical Physics 37, no. 5 (April 26, 2010): 2153–58. http://dx.doi.org/10.1118/1.3392287.
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Chow, N., A. Green, K. Hwang, C. Jack, P. Thompson, and L. Apostolova. "Comparison of Automated and Manual Hippocampal Segmentation (P03.101)." Neurology 78, Meeting Abstracts 1 (April 22, 2012): P03.101. http://dx.doi.org/10.1212/wnl.78.1_meetingabstracts.p03.101.
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Tingelhoff, Kathrin, Klaus W. G. Eichhorn, Ingo Wagner, Maria E. Kunkel, Analia I. Moral, Markus E. Rilk, Friedrich M. Wahl, and Friedrich Bootz. "Analysis of manual segmentation in paranasal CT images." European Archives of Oto-Rhino-Laryngology 265, no. 9 (February 6, 2008): 1061–70. http://dx.doi.org/10.1007/s00405-008-0594-z.
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Pal, Pralay. "Fast freeform hybrid reconstruction with manual mesh segmentation." International Journal of Advanced Manufacturing Technology 63, no. 9-12 (February 23, 2012): 1205–15. http://dx.doi.org/10.1007/s00170-012-3986-6.
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Zupan, Gašper, Dušan Šuput, Zvezdan Pirtošek, and Andrej Vovk. "Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra." Brain Sciences 9, no. 12 (November 22, 2019): 335. http://dx.doi.org/10.3390/brainsci9120335.
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In Parkinson’s disease (PD), there is a reduction of neuromelanin (NM) in the substantia nigra (SN). Manual quantification of the NM volume in the SN is unpractical and time-consuming; therefore, we aimed to quantify NM in the SN with a novel semi-automatic segmentation method. Twenty patients with PD and twelve healthy subjects (HC) were included in this study. T1-weighted spectral pre-saturation with inversion recovery (SPIR) images were acquired on a 3T scanner. Manual and semi-automatic atlas-free local statistics signature-based segmentations measured the surface and volume of SN, respectively. Midbrain volume (MV) was calculated to normalize the data. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of both methods. PD patients had significantly lower SN mean surface (37.7 ± 8.0 vs. 56.9 ± 6.6 mm2) and volume (235.1 ± 45.4 vs. 382.9 ± 100.5 mm3) than HC. After normalization with MV, the difference remained significant. For surface, sensitivity and specificity were 91.7 and 95 percent, respectively. For volume, sensitivity and specificity were 91.7 and 90 percent, respectively. Manual and semi-automatic segmentation methods of the SN reliably distinguished between PD patients and HC. ROC analysis shows the high sensitivity and specificity of both methods.
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Lee, Nayoung A., Carey E. Priebe, Michael I. Miller, and J. Tilak Ratnanather. "Validation of Alternating Kernel Mixture Method: Application to Tissue Segmentation of Cortical and Subcortical Structures." Journal of Biomedicine and Biotechnology 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/346129.
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This paper describes the application of the alternating Kernel mixture (AKM) segmentation algorithm to high resolution MRI subvolumes acquired from a 1.5T scanner (hippocampus,n=10and prefrontal cortex,n=9) and a 3T scanner (hippocampus,n=10and occipital lobe,n=10). Segmentation of the subvolumes into cerebrospinal fluid, gray matter, and white matter tissue is validated by comparison with manual segmentation. When compared with other segmentation methods that use traditional Bayesian segmentation, AKM yields smaller errors (P<.005, exact Wilcoxon signed rank test) demonstrating the robustness and wide applicability of AKM across different structures. By generating multiple mixtures for each tissue compartment, AKM mimics the increased variation of manual segmentation in partial volumes due to the highly folded tissues. AKM's superior performance makes it useful for tissue segmentation of subcortical and cortical structures in large-scale neuroimaging studies.
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Joshi, Akshita, Divesh Thaploo, Xiaoguang Yan, Theresa Herrmann, Hudaa Alrahman Khabour, and Thomas Hummel. "A novel technique for olfactory bulb measurements." PLOS ONE 15, no. 12 (December 16, 2020): e0243941. http://dx.doi.org/10.1371/journal.pone.0243941.
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Background To introduce new ways to calculate OB volumes, checking their validity and comparing them to already established technique i.e. OB volumetric based on manual segmentation of OB boundaries. Methods Two approaches were used to calculate OB volumes (1) Manual Segmentation using planimetric manual contouring; (2) Box-frame method, calculating the parameters based on a box placed around the OB. Results We calculated OB volumes using both techniques and found comparable outcomes. High inter-observer reliability was found for volumes calculated by both observers. For manual segmentation, Cronbach’s alpha (α) was 0.91 and 0.93 for right and left OB volume, respectively, whereas for the box-frame method α was 0.94 and 0.90 for right and left OB, respectively. Conclusions The simple box-frame method of OB volume calculation appears reliable. Its results are comparable to an established technique.
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Collins, D. L., and A. C. Evans. "Animal: Validation and Applications of Nonlinear Registration-Based Segmentation." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 08 (December 1997): 1271–94. http://dx.doi.org/10.1142/s0218001497000597.
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Magnetic resonance imaging (MRI) has become the modality of choice for neuro-anatomical imaging. Quantitative analysis requires the accurate and reproducible labeling of all voxels in any given structure within the brain. Since manual labeling is prohibitively time-consuming and error-prone we have designed an automated procedure called ANIMAL (Automatic Nonlinear Image Matching and Anatomical Labeling) to objectively segment gross anatomical structures from 3D MRIs of normal brains. The procedure is based on nonlinear registration with a previously labeled target brain, followed by numerical inverse transformation of the labels to the native MRI space. Besides segmentation, ANIMAL has been applied to non-rigid registration and to the analysis of morphometric variability. In this paper, the nonlinear registration approach is validated on five test volumes, produced with simulated deformations. Experiments show that the ANIMAL recovers 64% of the nonlinear residual variability remaining after linear registration. Segmentations of the same test data are presented as well. The paper concludes with two applications of ANIMAL using real data. In the first, one MRI volume is nonlinearly matched to a second and is automatically segmented using labels, predefined on the second MRI volume. The automatic segmentation compares well with manual labeling of the same structures. In the second application, ANIMAL is applied to seventeen MRI data sets, and a 3D map of anatomical variability estimates is produced. The automatic variability estimates correlate well (r =0.867, p = 0.01) with manual estimates of inter-subject variability.
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McGrath, Hari, Peichao Li, Reuben Dorent, Robert Bradford, Shakeel Saeed, Sotirios Bisdas, Sebastien Ourselin, Jonathan Shapey, and Tom Vercauteren. "Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI." International Journal of Computer Assisted Radiology and Surgery 15, no. 9 (July 16, 2020): 1445–55. http://dx.doi.org/10.1007/s11548-020-02222-y.
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Rehman, Murk, and Pertab Rai. "QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION." International Journal of Engineering Technologies and Management Research 6, no. 5 (March 25, 2020): 95–100. http://dx.doi.org/10.29121/ijetmr.v6.i5.2019.375.
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The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithms. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in the diagnosis of the pleural disease. Pleural effusion is the collection of excess fluid in the pleural cavity. Excessive amount of fluid can impair breathing by limiting the expansion of lungs. Heart failure, cancer, cirrhosis, pneumonia, tuberculosis and many other are the causes of pleural effusion. A number of noninvasive imaging techniques such as radiography, ultrasound and computed tomography (CT) can detect the pleural effusion. The problem faced is the quantification of pleural effusion volume for the purpose of diagnosis of the pleural disease. The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithm. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in diagnosis of the pleural disease. The results obtained by both the aforementioned techniques indicate that the manual segmentation is better because automated technique has less number of pixels.
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Waymont, Jennifer M. J., Chariklia Petsa, Chris J. McNeil, Alison D. Murray, and Gordon D. Waiter. "Validation and comparison of two automated methods for quantifying brain white matter hyperintensities of presumed vascular origin." Journal of International Medical Research 48, no. 2 (October 15, 2019): 030006051988005. http://dx.doi.org/10.1177/0300060519880053.
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Objectives White matter hyperintensities (WMH) are a common imaging finding indicative of cerebral small vessel disease. Lesion segmentation algorithms have been developed to overcome issues arising from visual rating scales. In this study, we evaluated two automated methods and compared them to visual and manual segmentation to determine the most robust algorithm provided by the open-source Lesion Segmentation Toolbox (LST). Methods We compared WMH data from visual ratings (Scheltens’ scale) with those derived from algorithms provided within LST. We then compared spatial and volumetric WMH data derived from manually-delineated lesion maps with WMH data and lesion maps provided by the LST algorithms. Results We identified optimal initial thresholds for algorithms provided by LST compared with visual ratings (Lesion Growth Algorithm (LGA): initial κ and lesion probability thresholds, 0.5; Lesion Probability Algorithm (LPA) lesion probability threshold, 0.65). LGA was found to perform better then LPA compared with manual segmentation. Conclusion LGA appeared to be the most suitable algorithm for quantifying WMH in relation to cerebral small vessel disease, compared with Scheltens’ score and manual segmentation. LGA offers a user-friendly, effective WMH segmentation method in the research environment.
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Comelli, Albert, Navdeep Dahiya, Alessandro Stefano, Federica Vernuccio, Marzia Portoghese, Giuseppe Cutaia, Alberto Bruno, Giuseppe Salvaggio, and Anthony Yezzi. "Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging." Applied Sciences 11, no. 2 (January 15, 2021): 782. http://dx.doi.org/10.3390/app11020782.
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Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.
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D, Kishore. "Brain Tumor Classification and Segmentation using Mask R-CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 667–68. http://dx.doi.org/10.22214/ijraset.2021.36440.
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MRI segmentation is a crucial task in many clinical applications. A variety of approaches for brain analysis rely on accurate segmentation of anatomical regions. Quantitative analysis of brain MRI has been used extensively for the characterization of brain disorders such as Alzheimer’s, epilepsy, schizophrenia, multiple sclerosis, cancer, and many infectious, degenerative diseases. Manual Segmentation requires outlining structures slice-by-slice, it is not only expensive and tedious but also inaccurate due to human error. Also, manual segmentation is extremely time-consuming and initial hours of brain tumor and strokes are crucial to diagnose it. Therefore, automated segmentation procedures are needed to ensure accuracy close to that of experts with high consistency. We propose to create a Deep Learning based Brain Segmentation solution that would fully automate the process of Brain Tumor Segmentation to solve those cases which are generally missed by the human eye and save time.
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Pijpker, Peter A. J., Tim S. Oosterhuis, Max J. H. Witjes, Chris Faber, Peter M. A. van Ooijen, Jiří Kosinka, Jos M. A. Kuijlen, Rob J. M. Groen, and Joep Kraeima. "A semi-automatic seed point-based method for separation of individual vertebrae in 3D surface meshes: a proof of principle study." International Journal of Computer Assisted Radiology and Surgery 16, no. 9 (May 27, 2021): 1447–57. http://dx.doi.org/10.1007/s11548-021-02407-z.
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Abstract Purpose The purpose of this paper is to present and validate a new semi-automated 3D surface mesh segmentation approach that optimizes the laborious individual human vertebrae separation in the spinal virtual surgical planning workflow and make a direct accuracy and segmentation time comparison with current standard segmentation method. Methods The proposed semi-automatic method uses the 3D bone surface derived from CT image data for seed point-based 3D mesh partitioning. The accuracy of the proposed method was evaluated on a representative patient dataset. In addition, the influence of the number of used seed points was studied. The investigators analyzed whether there was a reduction in segmentation time when compared to manual segmentation. Surface-to-surface accuracy measurements were applied to assess the concordance with the manual segmentation. Results The results demonstrated a statically significant reduction in segmentation time, while maintaining a high accuracy compared to the manual segmentation. A considerably smaller error was found when increasing the number of seed points. Anatomical regions that include articulating areas tend to show the highest errors, while the posterior laminar surface yielded an almost negligible error. Conclusion A novel seed point initiated surface based segmentation method for the laborious individual human vertebrae separation was presented. This proof-of-principle study demonstrated the accuracy of the proposed method on a clinical CT image dataset and its feasibility for spinal virtual surgical planning applications.
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Moen, M. A. N., A. P. Doulgeris, S. N. Anfinsen, A. H. H. Renner, N. Hughes, S. Gerland, and T. Eltoft. "Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts." Cryosphere Discussions 7, no. 3 (June 13, 2013): 2595–634. http://dx.doi.org/10.5194/tcd-7-2595-2013.
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Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in-situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.
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Tufvesson, Jane, Erik Hedström, Katarina Steding-Ehrenborg, Marcus Carlsson, Håkan Arheden, and Einar Heiberg. "Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging." BioMed Research International 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/970357.
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Introduction.Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.Methods.Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training setn=40, test setn=50). Manual delineation was reference standard and second observer analysis was performed in a subset (n=25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.Results.The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set.Conclusions.The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.