Relevant bibliographies by topics / Manual segmentation

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Academic literature on the topic 'Manual segmentation'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Manual segmentation"

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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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Dissertations / Theses on the topic "Manual segmentation"

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Naeslund, Elin. "Stroke Lesion Segmentation for tDCS." Thesis, Linköpings universitet, Medicinsk informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71472.

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Transcranial direct current stimulation (tDCS), together with speech therapy, is known to relieve the symptoms of aphasia. Knowledge about amount of current to apply and stimulation location is needed to ensure the best result possible. Segmented tissues are used in a finite element method (FEM) simulation and by creating a mesh, information to guide the stimulation is gained. Thus, correct segmentation is crucial. Manual segmentation is known to produce the most accurate result, although it is not useful in the clinical setting since it currently takes weeks to manually segment one image volume. Automatic segmentation is faster, although both acute stroke lesions and nectrotic stroke lesions are known to cause problems. Three automatic segmentation routines are evaluated using default settings and two sets of tissue probability maps (TPMs). Two sets of stroke patients are used; one set with acute stroke lesions (which can only be seen as a change in image intensity) and one set with necrotic stroke lesions (which are cleared out and filled with cerebrospinal fluid (CSF)). The original segmentation routine in SPM8 does not produce correct segmentation result having problems with lesion and paralesional areas. Mohamed Seghier’s ALI, an automatic segmentation routine developed to handle lesions as an own tissue class, does not produce satisfactory result. The new segmentation routine in SPM8 produces the best results, especially if Chris Rorden’s (professor at The Georgia Institute of Technology) improved TPMs are used. Unfortunately, the layer of CSF is not continuous. The segmentation result can still be used in a FEM simulation, although the result from the simulatation will not be ideal. Neither of the automatic segmentation routines evaluated produce an acceptable result (see Figure 5.7) for stroke patients. Necrotic stroke lesions does not affect the segmentation result as much as the acute dito, especially if there is only a small amount of scar tissue present at the lesion site. The new segmentation routine in SPM8 has the brightest future, although changes need to be made to ensure anatomically correct segmentation results. Post-processing algorithms, relying on morphological prior constraints, can improve the segmentation result further.
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Strapp, Ashley. "The feasibility of utilizing sonographic image segmentation to evaulate [sic] axillary lymph nodes automated computer software vs. manual segmentation /." Connect to resource, 2010. http://hdl.handle.net/1811/45364.

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Green, Eric R. "SEGMENTATION STRATEGIES FOR ROAD SAFETY ANALYSIS." UKnowledge, 2018. https://uknowledge.uky.edu/ce_etds/62.

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This dissertation addresses the relationship between roadway segment length and roadway attributes and their relationship to the efficacy of Safety Performance Function (SPF) models. This research focuses on three aspects of segmentation: segment length, roadway attributes, and combinations of the two. First, it is shown that choice of average roadway segment length can result in markedly different priority lists. This leads to an investigation of the effect of segment length on the development of SPFs and identifies average lengths that produce the best-fitting SPF. Secondly, roadway attributes are filtered to test the effect that homogeneity has on SPF development. Lastly, a combination of segment length and attributes are examined in the same context. In the process of conducting this research a tool was developed that provides objective goodness-of-fit measures as well as visual depictions of the model. This information can be used to avoid things like omitted variable bias by allowing the user to include other variables or filter the database. This dissertation also discusses and offers examples of ways to improve the models by employing alternate model forms. This research revealed that SPF development is sensitive to a variety of factors related to segment length and attributes. It is clear that strict base condition filters based on the most predominant roadway attributes provide the best models. The preferred functional form was shown to be dependent on the segmentation approach (fixed versus variable length). Overall, an important step in SPF development process is evaluation and comparison to determine the ideal length and attributes for the network being analyzed (about 2 miles or 3.2 km for Kentucky parkways). As such, a framework is provided to help safety professionals employ the findings from this research.
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Dionísio, Fernando Carrasco Ferreira. "Avaliação da reprodutibilidade intra e interobservador da segmentação manual de sarcomas ósseos em imagens de ressonância magnética." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/17/17158/tde-10042018-165710/.

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Os sarcomas ósseos representam uma proporção significativa de tumores na faixa etária pediátrica, ainda apresentando um quadro desafiador devido a sua significativa taxa de morbimortalidade. Pesquisas para o desenvolvimento de novas modalidades terapêuticas e para o desenvolvimento de métodos que identifiquem características da doença que possam permitir melhor estratificação dos pacientes através de dados clinicamente relevantes para individualizar as condutas clínicas são necessárias. Dentro deste contexto surge o conceito de radiômica, que visa extrair dados clinicamente relevantes a partir de imagens médicas. Entretanto, para colocar a radiômica em prática, é necessário selecionar, nas imagens médicas, as áreas de interesse referentes às patologias estudadas, e este processo se denomina segmentação. O objetivo primário deste estudo foi avaliar a reprodutibilidade intra e inter-observador da segmentação manual de sarcomas ósseos em imagens de ressonância magnética (RM). Como objetivo secundário, foi avaliada a capacidade da segmentação semiautomática em reduzir o tempo necessário para segmentação, mantendo similaridade com a segmentação manual. O estudo foi realizado de forma retrospectiva com inclusão de pacientes com diagnóstico de osteossarcoma ou sarcoma de Ewing confirmado por estudo histopatológico e que tivessem imagens de RM realizadas no Hospital Universitário de nossa Instituição realizadas previamente a qualquer intervenção terapêutica. Três médicos radiologistas, de forma independente e às cegas em relação as demais segmentações e em relação ao resultado histopatológico, realizaram a segmentação manual dos contornos destes tumores utilizando o software 3DSlicer, permitindo que fosse realizada avaliação da reprodutibilidade interobservador. Um dos radiologistas realizou uma segunda segmentação manual dos mesmos casos, possibilitando a avaliação da reprodutibilidade intraobservador, e, ainda, uma terceira segmentação foi realizada, utilizando metodologia semiautomática, disponível no software mencionado. Para a análise estatística, foi utilizado o coeficiente de similaridade de Dice (DICE), a distância Hausdorff (DH), comparações de volumes e análises dos intervalos de tempo necessários para realização das segmentações. Os parâmetros avaliados demonstraram haver boa reprodutibilidade intraobservador, com DICE variando entre 0,83 a 0,97; e distância Hausdorff variando entre 3,37 a 28,73 mm. Também foi demonstrada boa reprodutibilidade interobservador com DICE variando entre 0,73 a 0,97; e distância Hausdorff variando entre 3,93 a 33,40 mm. A segmentação semiautomática demonstrou boa similaridade em relação à segmentação manual (DICE variando entre 0,71 a 0,96 e DH variando entre 5,38 a 31,54 mm), havendo redução significativa do tempo necessário para segmentação. Entre todas as situações comparadas, os volumes não apresentaram diferenças estatisticamente significativas (p-valor>0,05).
Bone sarcomas represent a significant proportion of tumors in the pediatric age group and they still are a challenge due to their significant morbidity and mortality rates. Reseaches are important for the development of new therapeutic modalities and for the development of methods that identify features that allow better stratification of the patients with theses diseases for individualization of their treatments. In this context emerges the concept of radiomics, which is the process of extraction of clinically relevant data from medical images. It is important to segment the areas of interest im medical images for the pratice of this process. The primary objective of this study was to evaluate the intra- and interobserver reproducibility of manual segmentation of bone sarcomas on magnetic resonance imaging (MRI). As a secondary objective, it was evaluated if the semiautomatic segmentation could be similar to manual segmentation and if the semiautomatic method could reduce the time required for segmentation. The study was performed retrospectively with the inclusion of patients with osteosarcoma or Ewing sarcoma confirmed by histopathological study and who had MRI performed at the University Hospital of our Institution prior to any therapeutic intervention. Three radiologists, independently and blindly in relation to the other segmentations and in relation to the histopathological results, performed the manual segmentation of the contours of these tumors using 3DSlicer software, allowing an interobserver reproducibility evaluation. One of the radiologists performed a second manual segmentation of the same cases, allowing the evaluation of intraobserver reproducibility. A third segmentation was performed, using semi-automatic methodology, available in the mentioned software. For the statistical analysis, Dice similarity coefficient (DICE), Hausdorff distance (DH), comparisons between volumes and time intervals for segmentations were used. The parameters evaluated demonstrated a good intraobserver reproducibility, with DICE ranging from 0.83 to 0.97 and Hausdorff distance ranging from 3.37 to 28.73 mm. Good interobserver reproducibility was also demonstrated with DICE ranging from 0.73 to 0.97 and Hausdorff distance ranging from 3.93 to 33.40 mm. Semiautomatic segmentation demonstrated good similarity to manual segmentation (DICE ranging from 0.71 to 0.96 and HD ranging from 5.38 to 31.54mm), and there was significant reduction in the time required for segmentation. Among all the situations compared, the volumes did not present significant statistical differences (p-value> 0.05).
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Viall, Sarah F. "The feasibility of conducting manual image segmentation of 3D sonographic images of axillary lymph nodes." Connect to resource, 2009. http://hdl.handle.net/1811/36945.

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Isensee, Fabian [Verfasser], and Benedikt [Akademischer Betreuer] Brors. "From Manual to Automated Design of Biomedical Semantic Segmentation Methods / Fabian Isensee ; Betreuer: Benedikt Brors." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1226541739/34.

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Süssová, Zuzana. "Design manuál a marketingová komunikace outdoorové firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-225086.

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Magister thesis is aimed at basic design components creation for the new company operating in the field of selling outdoor clothing. With regard to design creation it will be also marketing communication designed. As particular objectives of this thesis there will be analysis of cultural background made, follow-up market segmentation and other components of marketing mix designed. As a result should be proposal of complex marketing as the design elements and relevant marketing communication. It should be designed with regard to the company and market conditions.
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Oliveira, Larissa Santos. "Avaliação da reprodutibilidade intra e interobservador da segmentação manual dos sarcomas de partes moles em imagens de ressonância magnética." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/17/17158/tde-23042018-103638/.

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Os sarcomas de partes moles constituem um grupo diverso de neoplasias que podem surgir nos tecidos conjuntivos praticamente de qualquer região do corpo. A ressonância magnética (RM) é atualmente o exame de escolha para detecção, estadiamento regional e acompanhamento desses tumores. A segmentação das lesões a partir das imagens de RM da rotina permite extrair dados quantitativos, que tem potencial para adicionar informações a análise. O propósito do estudo é avaliar a reprodutibilidade da segmentação manual de sarcomas de partes moles em imagens de RM de pacientes com diagnóstico definitivo confirmado por meio da histopatologia. Como objetivo secundário foi realizada a comparação da segmentação manual e semiautomática a fim de validar a segmentação semiautomática como método alternativo para segmentação desses tumores. Foi estudada uma coorte retrospectiva de 15 pacientes consecutivos com diagnóstico confirmado de sarcoma de partes moles acompanhados em nosso serviço, no período de janeiro de 2006 até janeiro de 2016, com imagens de RM adquiridas previamente ao tratamento e disponíveis para análise no formato DICOM. Foi utilizado o software 3D Slicer para realizar as segmentações pelos métodos manual e semiautomático. Três radiologistas fizeram as segmentações de forma independente e às cegas para permitir avaliação interobservador. Os resultados obtidos demonstram haver alta reprodutibilidade intraobservador com coeficiente de similaridade de Dice entre as segmentações variando de 0,849 a 0,979 e as distâncias Hausdorff variando de 3,53 mm a 20,96 mm e uma boa reprodutibilidade interobservador com coeficientes de similaridade de Dice variando de 0,741 a 0,972 e distâncias Hausdorff variando de 5,83 a 60,84 mm. Foi encontrada uma concordância substancial entre as segmentações realizadas pelo método semiautomático quando comparadas com as segmentações realizadas pelo método manual com coeficientes de similaridade de Dice variando de 0,871 a 0,973 e distâncias Hausdorff variando de 5,43 mm a 31,75 mm. Em relação ao tempo de segmentação não houve diferença estatisticamente significativa do método semiautomático quando comparado ao método manual (p>0,05). Também foram calculados os volumes obtidos nas diferentes segmentações e houve concordância quase perfeita entre as duas segmentações manuais realizadas pelo radiologista 1, entre as segmentações realizadas pelo radiologista 1 e pelo radiologista 2, entre as segmentações realizadas pelo radiologista 1 e pelo radiologista 3, e entre a segmentação manual e semiautomática realizadas pelo radiologista 1, sendo obtidos coeficientes de correlação intraclasse (ICC) entre 0,9927 e 0,9990. Os resultados obtidos demonstram boa reprodutibilidade intra e interobservador da segmentação manual utilizando o software 3D Slicer validando dessa forma esse método como ferramenta confiável para servir de padrão de referência em futuros estudos quantitativos desses tumores. Foi encontrada concordância quase perfeita entre as segmentações realizadas pelo método semiautomático quando comparadas com as segmentações realizadas pelo método manual, mas nossos resultados não demonstraram diferença significativa de tempo de segmentação do método semiautomático em relação ao método manual.
Soft tissue sarcomas constitute a diverse group of neoplasms that can arise in the connective tissues from virtually any region of the body. Magnetic resonance imaging (MRI) is currently the examination of choice for detection, regional staging and followup of these tumors. The segmentation of the lesions from the routine MR images allows the extraction of quantitative data, which has the potential to add information to the analysis. The purpose of the study is to evaluate the reproducibility of manual segmentation of soft tissue sarcomas in MRI images of patients with definitive diagnosis confirmed by histopathology. As a secondary objective, a comparison of manual and semiautomatic segmentation was performed to validate semiautomatic segmentation as an alternative method for segmentation of these tumors. We studied a retrospective cohort of 15 consecutive patients with confirmed diagnosis of soft tissue sarcoma accompanied at our service from January 2006 to January 2016 with MR images acquired prior to treatment and available for analysis in the DICOM format. The software was used 3D Slicer to perform segmentation by manual and semiautomatic methods. Three radiologists did the segmentations independently and blindly to allow inter-observer evaluation. The results obtained show high intraobserver reproducibility with Dice similarity coefficient between the segmentations ranging from 0.849 to 0.979 and Hausdorff distances ranging from 3.53 mm to 20.96 mm and good interobserver reproducibility with Dice similarity coefficients ranging from 0.741 to 0.972 and Hausdorff distances varying from 5.83 to 60.84 mm. A substantial agreement was found between the segmentations performed by the semiautomatic method when compared to the segmentations performed by the manual method with Dice similarity coefficients ranging from 0.871 to 0.973 and Hausdorff distances ranging from 5.43 mm to 31.75 mm. Regarding the segmentation time, there was no statistically significant difference of the semiautomatic method when compared to the manual method (p> 0.05). The volumes obtained in the different segmentations were also calculated and there was almost perfect agreement between the two manual segmentations performed by the radiologist 1, between the segmentations performed by radiologist 1 and radiologist 2, between the segmentations performed by radiologist 1 and radiologist 3, and between The manual and semi-automatic segmentation performed by the radiologist 1, and intraclass correlation coefficients (ICC) between 0.9927 and 0.9990 were obtained. The results obtained demonstrate good intra and interobserver reproducibility of the manual segmentation using 3D Slicer software, thus validating this method as a reliable tool to serve as a reference standard in future quantitative studies of these tumors. Almost perfect agreement was found between the segmentations performed by the semiautomatic method when compared to the segmentations performed by the manual method, but our results did not show a significant difference in segmentation time of the semiautomatic method in relation to the manual method.
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Hillis, Yingli, and Yingli Hillis. "Validation of a Semi-Automatic Cell Segmentation Method to the Manual Cell Counting Method on Identifying Proliferating Cells in 3-D Confocal Microscope Images." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/626739.

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Sphere-forming assay is an in-vitro technique to assess the self-renewal and differentiation potential of a homogenous or heterogenous population of cells. This technique is commonly used in the stem cell and cancer biology fields to assess the ability of a cell that is capable of self-proliferation and differentiation. (Schmitt, 2011, Lombaert et al., 2008) To detect proliferative growth, Ki-67, a marker of proliferation, is used in immunofluorescence staining of sphere-forming cells. The current gold standard methodology to quantify cell proliferation is to manually count the cells on images obtained using confocal microscopy. However, the reproducibility, the inter- and intra-subject variability, and the time requirement for manually counting cells are often major challenges for researchers. In this study, we propose a semi-automated cell segmentation algorithm using the FARSIGHT toolbox, to automatically count the individual three-dimensional (3-D) cell nuclei. The present work focused on the investigation of two aspects of the algorithm performance: sensitivity and specificity. We grouped images by sphere size to test specificity of the algorithm. For the sensitivity analysis, we tested the segmentation algorithm on both raw uncalibrated images and calibrated images using Fiji ImageJ software. We found that the proposed algorithm could efficiently identify cells and cell boundaries to overcome the background noise. Finally, statistical analysis showed the differentiation index had low percentage matching between the proposed method and the manual counting method.
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Guo, Yuhua. "The role of the basal ganglia in memory and motor inhibition." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/268489.

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This PhD thesis investigated the role of the basal ganglia in memory and motor inhibition. Recent neuroimaging evidence suggests a supramodal network of inhibition involving the lateral prefrontal cortex. Here we examined whether this supramodal network also includes subcortical structures, such as the basal ganglia. Despite their well-established role in motor control, the basal ganglia are repeatedly activated but never interpreted during memory inhibition. We first used a series of meta-analyses to confirm the consistent involvement of the basal ganglia across studies using memory and motor inhibition tasks (including the Go/No-Go, Think/No-Think, and Stop-signal tasks), and discovered that there may be different subprocesses of inhibition. For instance, while the Go/No-Go task may require preventing a response from taking place, the Think/No-Think and Stop-signal tasks may require cancelling an emerging or ongoing response. We then conducted an fMRI study to examine how the basal ganglia interact with other putative supramodal regions (e.g., DLPFC) to achieve memory and motor inhibition during prevention and cancellation. Through dynamic causal modelling (DCM), we found that both DLPFC and basal ganglia play effective roles to achieve inhibition in the task-specific regions (hippocampus for memory inhibition; primary motor cortex (M1) for motor inhibition). Specifically, memory inhibition requires a DLPFC-basal ganglia-hippocampus pathway, whereas motor inhibition requires a basal ganglia-DLPFC-M1 pathway. We correlated DCM coupling parameters with behavioural indices to examine the relationship between network dynamics during prevention and cancellation and the successfulness of inhibition. However, due to constraints with DCM parameter estimates, caution is necessary when interpreting these results. Finally, we used diffusion weighted imaging to explore the anatomical connections supporting functions and behaviour. Unfortunately, we were unable to detect any white matter variability in relation to effective connectivity or behaviour during the prevention or cancellation processes of memory and motor inhibition at this stage. This PhD thesis provides essential INITIAL evidence that not only are the basal ganglia consistently involved in memory and motor inhibition, but these structures are effectively engaged in these tasks, achieving inhibition through task-specific pathways. We will discuss our findings, interpretations, and future directions in the relevant chapters.
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Books on the topic "Manual segmentation"

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A, Whyte Wayne, and Lewis Research Center, eds. Numerical arc segmentation algorithm for a radio conference--NASARC (version 2.0): Technical manual. Cleveland, Ohio: National Aeronautics and Space Administration, Lewis Research Center, 1988.

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Book chapters on the topic "Manual segmentation"

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Wang, Hongzhi, and Paul A. Yushkevich. "Guiding Automatic Segmentation with Multiple Manual Segmentations." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 429–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33418-4_53.

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Oehmen, Raoul, Kim Kirsner, and Nicolas Fay. "Reliability of the Manual Segmentation of Pauses in Natural Speech." In Advances in Natural Language Processing, 263–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14770-8_30.

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Yepes-Calderon, Fernando, and J. Gordon McComb. "Manual Segmentation Errors in Medical Imaging. Proposing a Reliable Gold Standard." In Communications in Computer and Information Science, 230–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32475-9_17.

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Harrison, L. C. V., P. Dastidar, K. K. Holli, S. Savio, A. Autere, A. Oinonen, V. Pylkki, S. Soimakallio, and H. Eskola. "Manual Segmentation of Brain Tissue and Multiple Sclerosis Lesions for Texture Analysis." In IFMBE Proceedings, 300–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03879-2_85.

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Shi, Yinghuan, Wanqi Yang, Yang Gao, and Dinggang Shen. "Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?" In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 692–700. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66179-7_79.

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Sayers, Dean, Maged Salim Habib, and Bashir AL-Diri. "Manual Tool and Semi-automated Graph Theory Method for Layer Segmentation in Optical Coherence Tomography." In Advances in Intelligent Systems and Computing, 1090–109. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22871-2_78.

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Zhang, Jiaxin, Tomohiro Fukuda, and Nobuyoshi Yabuki. "A Large-Scale Measurement and Quantitative Analysis Method of Façade Color in the Urban Street Using Deep Learning." In Proceedings of the 2020 DigitalFUTURES, 93–102. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_9.

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AbstractColor planning has become a significant issue in urban development, and an overall cognition of the urban color identities will help to design a better urban environment. However, the previous measurement and analysis methods for the facade color in the urban street are limited to manual collection, which is challenging to carry out on a city scale. Recent emerging dataset street view image and deep learning have revealed the possibility to overcome the previous limits, thus bringing forward a research paradigm shift. In the experimental part, we disassemble the goal into three steps: firstly, capturing the street view images with coordinate information through the API provided by the street view service; then extracting facade images and cleaning up invalid data by using the deep-learning segmentation method; finally, calculating the dominant color based on the data on the Munsell Color System. Results can show whether the color status satisfies the requirements of its urban plan for façade color in the street. This method can help to realize the refined measurement of façade color using open source data, and has good universality in practice.
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Maier, Jennifer, Marianne Black, Mary Hall, Jang-Hwan Choi, Marc Levenston, Garry Gold, Rebecca Fahrig, Bjoern Eskofier, and Andreas Maier. "Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus." In Informatik aktuell, 86–91. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25326-4_21.

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Kaster, Frederik O., Bjoern H. Menze, Marc-André Weber, and Fred A. Hamprecht. "Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations." In Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging, 74–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18421-5_8.

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Murugan, R. "Implementation of Deep Learning Neural Network for Retinal Images." In Handbook of Research on Applications and Implementations of Machine Learning Techniques, 77–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9902-9.ch005.

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The retinal parts segmentation has been recognized as a key component in both ophthalmological and cardiovascular sickness analysis. The parts of retinal pictures, vessels, optic disc, and macula segmentations, will add to the indicative outcome. In any case, the manual segmentation of retinal parts is tedious and dreary work, and it additionally requires proficient aptitudes. This chapter proposes a supervised method to segment blood vessel utilizing deep learning methods. All the more explicitly, the proposed part has connected the completely convolutional network, which is normally used to perform semantic segmentation undertaking with exchange learning. The convolutional neural system has turned out to be an amazing asset for a few computer vision assignments. As of late, restorative picture investigation bunches over the world are rapidly entering this field and applying convolutional neural systems and other deep learning philosophies to a wide assortment of uses, and uncommon outcomes are rising constantly.
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Conference papers on the topic "Manual segmentation"

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Duchesne, Simon, Fernando Valdivia, Nicolas Robitaille, F. Abiel Valdivia, Martina Bocchetta, Marina Boccardi, Clifford R. Jack, and Giovanni B. Frisoni. "Manual segmentation certification platform." In 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2013. http://dx.doi.org/10.1109/memea.2013.6549701.

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Renner, Johan, Roland Gårdhagen, and Matts Karlsson. "Subject Specific In-Vivo CFD Estimated Aortic WSS: Comparison Between Manual and Automated Segmentation Methods." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192735.

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When making computational fluid dynamics (CFD) based estimations of wall shear stress (WSS) in the human aorta, medical image converting processes to 3D geometries are important as the result is strongly dependent on the quality of the geometry [1]. The image interpretation process or segmentation can be more or less automated; however in clinical work today the gold standard is to manually interpret the medical image information. This combined magnetic resonance imaging (MRI) and CFD method aims to estimate WSS in human arteries in-vivo as WSS is strongly linked to atherosclerosis [2]. More or less automated segmentation has been used in previous studies but normally based on a stack of 2D individually segmented slices which is combined into a 3D model [3]. The aim of this work is to compare manual 2D and automatic 3D segmentations.
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Kutucu, Hakan, Cagdas Eker, Omer Kitis, and Ali Saffet Gonul. "Comparison of atlas based segmentation and manual segmentation of hippocampus." In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130369.

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Venugopal, Prem, Xia Li, Roshni Bhagalia, Peter M. Edic, and Lishui Cheng. "Sensitivity of FFR-CT to manual segmentation." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2018. http://dx.doi.org/10.1117/12.2292836.

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Gupta, Rashmi, and Cathal Gurrin. "Considering Manual Annotations in Dynamic Segmentation of Multimodal Lifelog Data." In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2019. http://dx.doi.org/10.1109/percomw.2019.8730649.

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Wang, Shichang, Chu-Ren Huang, Yao Yao, and Angel Chan. "Create a Manual Chinese Word Segmentation Dataset Using Crowdsourcing Method." In Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-3102.

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Haller, John W., Gary E. Christensen, Michael I. Miller, Sarang C. Joshi, Mokhtar Gado, John G. Csernansky, and Michael W. Vannier. "Comparison of automated and manual segmentation of hippocampus MR images." In Medical Imaging 1995, edited by Murray H. Loew. SPIE, 1995. http://dx.doi.org/10.1117/12.208692.

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Shum, Judy, Adam Goldhammer, Elena DiMartino, and Ender Finol. "CT Imaging of Abdominal Aortic Aneurysms: Semi-Automatic Vessel Wall Detection and Quantification of Wall Thickness." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192638.

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Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may provide useful information to predict rupture risk. Our procedure for estimating wall thickness in AAAs includes medical image segmentation and wall thickness detection. Image segmentation requires identifying and segmenting the luminal and outer wall boundaries of the blood vessels and wall thickness can be calculated by using intensity histograms and neural networks. The goal of this study is to develop an image-based, semi-automated method to trace the contours of the vessel wall and measure the wall thickness of the abdominal aorta from in-vivo, contrast-enhanced, CT images. An algorithm for the lumen and inner wall segmentations, and wall thickness detection was developed and tested on 10 ruptured and 10 unruptured AAAs. Reproducibility and repeatability of the algorithm were determined by comparing manual tracings made by two observers to contours made automatically by the algorithm itself. There was a high correspondence between automatic and manual area measurements for the lumen (r = 0.96) and between users (r = 0.98). Based on statistical analyses, the algorithm tends to underestimate the lumen area when compared to both observers.
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Guo, Lei, Lijian Zhou, Shaohui Jia, Li Yi, Haichong Yu, and Xiaoming Han. "An Automatic Segmentation Algorithm Used in Pipeline Integrity Alignment Sheet Design." In 2010 8th International Pipeline Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ipc2010-31036.

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Pipeline segmentation design is the first step to design alignment sheet. In this step, several rectangular boxes are used to cover pipeline and each box will become the basic unit of alignment sheet design. After studying various pipeline alignment sheet mapping technologies, the author found that traditional manual design method, which can take advantage of designers’ subjectivity, causes low work efficiency. By reviewing and studying existing works at home and abroad, the author believed that it is possible and feasible to develop an automatic segmentation algorithm based on existing curve simplification algorithms to improve to improve the efficiency of pipeline section design and alignment sheet mapping. Based on several classical curve simplification algorithms, the author proposed the automatic segmentation algorithm, which automatically adjusts the location of rectangular boxes according to the number of pipeline/circle intersection points and pipeline/ rectangular box intersection points. Finally, through comparing time and result with the traditional manual method, the author proved the algorithm’s effectiveness and feasibility.
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Power, Gregory J., and Robert A. Weisenseel. "ATR subsystem performance measures using manual segmentation of SAR target chips." In AeroSense '99, edited by Edmund G. Zelnio. SPIE, 1999. http://dx.doi.org/10.1117/12.357683.

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