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Journal articles on the topic 'Automated Segmentation Method'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, et al. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.

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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral

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Boellaard, Thierry N., Roy van Erck, Sophia H. van der Graaf, et al. "Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy." Diagnostics 15, no. 9 (2025): 1141. https://doi.org/10.3390/diagnostics15091141.

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Background: Robot-assisted radical prostatectomy (RARP) is a common treatment option for prostate cancer. A 3D model for surgical guidance can improve surgical outcomes. Manual expert radiologist segmentation of the prostate and tumor in prostate MRI to create 3D models is labor-intensive and prone to inter-observer variability, highlighting the need for automated segmentation methods. Methods: This study evaluates the performance of the prostate and tumor segmentation using a commercially available AI tool without (fully automated) and with manual adjustment (AI-assisted) compared to manual s

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Wang, Yang, Yihao Chen, Hao Yuan, and Cheng Wu. "An automated learning method of semantic segmentation for train autonomous driving environment understanding." International Journal of Advances in Intelligent Informatics 10, no. 1 (2024): 148. http://dx.doi.org/10.26555/ijain.v10i1.1521.

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One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As one of the most fundamental and challenging problems in autonomous driving, environment understanding has been widely studied. It directly determines whether the entire in-vehicle system can effectively identify surrounding objects of vehicles and make correct path planning. Semantic segmentation is the most important means of environment understanding among the many image recognition algorithms used in autonomous driving. However, the success of semantic segmentatio

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Kemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, et al. "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 (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 Initiat

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Buser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, et al. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients." Cancers 15, no. 7 (2023): 2115. http://dx.doi.org/10.3390/cancers15072115.

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Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these

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Matin-Mann, Farnaz, Ziwen Gao, Chunjiang Wei, et al. "Development and In-Silico and Ex-Vivo Validation of a Software for a Semi-Automated Segmentation of the Round Window Niche to Design a Patient Specific Implant to Treat Inner Ear Disorders." Journal of Imaging 9, no. 2 (2023): 51. http://dx.doi.org/10.3390/jimaging9020051.

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The aim of this study was to develop and validate a semi-automated segmentation approach that identifies the round window niche (RWN) and round window membrane (RWM) for use in the development of patient individualized round window niche implants (RNI) to treat inner ear disorders. Twenty cone beam computed tomography (CBCT) datasets of unilateral temporal bones of patients were included in the study. Defined anatomical landmarks such as the RWM were used to develop a customized 3D Slicer™ plugin for semi-automated segmentation of the RWN. Two otolaryngologists (User 1 and User 2) segmented th

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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, et al. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585

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Clark, A. E., B. Biffi, R. Sivera, et al. "Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images." Royal Society Open Science 7, no. 11 (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 refer

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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 (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 assistan

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Tran, Anh T., Dmitriy Desser, Tal Zeevi, et al. "Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography." Applied Sciences 15, no. 1 (2024): 111. https://doi.org/10.3390/app15010111.

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Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to

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Nishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, et al. "Deep generative models for automated muscle segmentation in computed tomography scanning." PLOS ONE 16, no. 9 (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 c

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G, Mohandass, Hari Krishnan G, and Hemalatha R J. "An approach to automated retinal layer segmentation in SDOCT images." International Journal of Engineering & Technology 7, no. 2.25 (2018): 56. http://dx.doi.org/10.14419/ijet.v7i2.25.12371.

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The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise de

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Halawa, Abdelrahman, Shehab Gamalel-Din, and Abdurrahman Nasr. "EXPLOITING BERT FOR MALFORMED SEGMENTATION DETECTION TO IMPROVE SCIENTIFIC WRITINGS." Applied Computer Science 19, no. 2 (2023): 126–41. http://dx.doi.org/10.35784/acs-2023-20.

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Writing a well-structured scientific documents, such as articles and theses, is vital for comprehending the document's argumentation and understanding its messages. Furthermore, it has an impact on the efficiency and time required for studying the document. Proper document segmentation also yields better results when employing automated Natural Language Processing (NLP) manipulation algorithms, including summarization and other information retrieval and analysis functions. Unfortunately, inexperienced writers, such as young researchers and graduate students, often struggle to produce well-stru

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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 (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

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Yang, Xin, Chaoyue Liu, Hung Le Minh, Zhiwei Wang, Aichi Chien, and Kwang-Ting (Tim) Cheng. "An automated method for accurate vessel segmentation." Physics in Medicine and Biology 62, no. 9 (2017): 3757–78. http://dx.doi.org/10.1088/1361-6560/aa6418.

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Wang, Zhe, Zhenyi Zhu, Yong Wu, et al. "Automated Tunnel Point Cloud Segmentation and Extraction Method." Applied Sciences 15, no. 6 (2025): 2926. https://doi.org/10.3390/app15062926.

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To address the issue of inaccurate tunnel segmentation caused by solely relying on point cloud coordinates, this paper proposes two algorithms, GuSAC and TMatch, along with a ring-based cross-section extraction method to achieve high-precision tunnel lining segmentation and cross-section extraction. GuSAC, based on the RANSAC algorithm, introduces a minimum spanning tree to reconstruct the topological structure of the tunnel design axis. By using a sliding window, it effectively distinguishes between curved and straight sections of long tunnels while removing non-tunnel structural point clouds

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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 (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

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MASSOPTIER, LAURENT, AVISHKAR MISRA, ARCOT SOWMYA, and SERGIO CASCIARO. "COMBINING GRAPH-CUT TECHNIQUE AND ANATOMICAL KNOWLEDGE FOR AUTOMATIC SEGMENTATION OF LUNGS AFFECTED BY DIFFUSE PARENCHYMAL DISEASE IN HRCT IMAGES." International Journal of Image and Graphics 11, no. 04 (2011): 509–29. http://dx.doi.org/10.1142/s0219467811004202.

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Accurate and automated lung segmentation in high-resolution computed tomography (HRCT) is highly challenged by the presence of pathologies affecting lung parenchyma appearance and borders. The algorithm presented employs an anatomical model-driven approach and systematic incremental knowledge acquisition to produce coarse lung delineation, used as initialization for the graph-cut algorithm. The proposed method is evaluated on a 49 HRCT cases dataset including various lung disease patterns. The accuracy of the method is assessed using dice similarity coefficient (DSC) and shape differentiation

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Pociask, Elżbieta, Krzysztof Piotr Malinowski, Magdalena Ślęzak, Joanna Jaworek-Korjakowska, Wojciech Wojakowski, and Tomasz Roleder. "Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography." Journal of Healthcare Engineering 2018 (December 26, 2018): 1–13. http://dx.doi.org/10.1155/2018/1414076.

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Background. Optical coherence tomography (OCT) is an innovative imaging technique that generates high-resolution intracoronary images. In the last few years, the need for more precise analysis regarding coronary artery disease to achieve optimal treatment has made intravascular imaging an area of primary importance in interventional cardiology. One of the main challenges in OCT image analysis is the accurate detection of lumen which is significant for the further prognosis. Method. In this research, we present a new approach to the segmentation of lumen in OCT images. The proposed work is focu

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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 (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 marg

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Jin, Felix Q., Anna E. Knight, Adela R. Cardones, Kathryn R. Nightingale, and Mark L. Palmeri. "Semi-automated weak annotation for deep neural network skin thickness measurement." Ultrasonic Imaging 43, no. 4 (2021): 167–74. http://dx.doi.org/10.1177/01617346211014138.

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Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only insp

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Golkar, Ehsan, Hossein Rabbani, and Ashrani Aizzuddin Abd. Rahni. "Inter-subject Registration-based Segmentation of Thoracic-Abdominal Organs in 4 Dimensional Magnetic Resonance Imaging." Jurnal Kejuruteraan 33, no. 4 (2021): 1045–51. http://dx.doi.org/10.17576/jkukm-2021-33(4)-26.

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4 Dimensional Magnetic Resonance Imaging (4D MRI) is currently gaining attention as an imaging modality which is able to capture inter-cycle variability of respiratory motion. Such information is beneficial for example in radiotherapy planning and delivery. In the latter case, there may be a need for organ segmentation, however 4D MRI are of low contrast, which complicates automated organ segmentation. This paper proposes a multi-subject thoracic-abdominal organ segmentation propagation scheme for 4D MRI. The proposed scheme is registration based, hence different combinations of deformation an

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Santone, Antonella, Rosamaria De Vivo, Laura Recchia, Mario Cesarelli, and Francesco Mercaldo. "A Method for Retina Segmentation by Means of U-Net Network." Electronics 13, no. 22 (2024): 4340. http://dx.doi.org/10.3390/electronics13224340.

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Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting perfor

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Sun, Yusen, Xingji Jin, Timo Pukkala, and Fengri Li. "A Comparison of Four Methods for Automatic Delineation of Tree Stands from Grids of LiDAR Metrics." Remote Sensing 14, no. 24 (2022): 6192. http://dx.doi.org/10.3390/rs14246192.

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Increased use of laser scanning in forest inventories is leading to the adoption and development of automated stand delineation methods. The most common categories of these methods are region merging and region growing. However, recent literature proposes alternative methods that are based on the ideas of cellular automata, self-organizing maps, and combinatorial optimization. The studies where these methods have been described suggest that the new methods are potential options for the automated segmentation of a forest into homogeneous stands. However, no studies are available that compare th

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Choi, Woorim, Chul-Ho Kim, Hyein Yoo, Hee Rim Yun, Da-Wit Kim, and Ji Wan Kim. "Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study." BMJ Open 14, no. 5 (2024): e079417. http://dx.doi.org/10.1136/bmjopen-2023-079417.

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ObjectivesWe aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently.MethodsWe used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement.ResultsThe Dice similarity coefficient was used to compare the manual segmentation with autom

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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 (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 netw

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Jiang, Huiyan, Shaojie Li, and Siqi Li. "Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation." BioMed Research International 2018 (September 24, 2018): 1–11. http://dx.doi.org/10.1155/2018/8536854.

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The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning m

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Bouzid-Daho, Abdellatif, Naima Sofi, Schahrazad Soltane, and Patrick Siarry. "Automated detection in microscopic images using segmentation." Brazilian Journal of Technology 7, no. 2 (2024): e69317. http://dx.doi.org/10.38152/bjtv7n2-003.

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In this paper, we present a segmentation clusteringbased approach for automated object detection. This paper deals with the segmentation and classification of blood cells for the purpose of detecting leukemia (abnormal blood cells). After the image acquisition and the preprocessing step, we proceeded to the application of the k-means method. In order to show the interest of the proposed approach, we present the different cancerous regions identified with their characteristics for biomedical diagnostic aid. The proposed method is tested on image dataset and achieves 98% segmentation accuracy. T

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Lee, Seyoung, Kai Zhang, Jeeyeon Lee, et al. "Abstract 2595: Accelerated and precise tumor segmentation in NSCLC: A comparative analysis of automated ClickSeg and manual annotation for radiomics." Cancer Research 84, no. 6_Supplement (2024): 2595. http://dx.doi.org/10.1158/1538-7445.am2024-2595.

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Abstract Background: Radiomics models utilizing artificial intelligence are being explored as a potential biomarker in the field of oncology. Radiomics analysis requires segmentations of radiographic imaging. However, manual segmentation is a labor-intensive process that is time consuming, and acts as a major rate-limiting step. Thus the development of automated segmentation tools presents an opportunity for innovation in regards to efficiency and precision. Our study aims to explore the feasibility of autosegmentation in comparison with manual segmentation. Methods: A cohort of 105 stage III-

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Pang, Shumao. "Three-Dimensional Lumbosacral Reconstruction by An Artificial Intelligence-Based Automated MR Image Segmentation for Selecting the Approach of Percutaneous Endoscopic Lumbar Discectomy." Pain Physician Journal 27, no. 2 (2024): E245—E254. https://doi.org/10.36076/ppj.2024.27.e245.

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BACKGROUND: Assessing the 3-dimensional (3D) relationship between critical anatomical structures and the surgical channel can help select percutaneous endoscopic lumbar discectomy (PELD) approaches, especially at the L5/S1 level. However, previous evaluation methods for PELD were mainly assessed using 2-dimensional (2D) medical images, making the understanding of the 3D relationship of lumbosacral structures difficult. Artificial intelligence based on automated magnetic resonance (MR) image segmentation has the benefit of 3D reconstruction of medical images. OBJECTIVES: We developed and valida

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Moëll, Mattias K., and Lloyd A. Donaldson. "COMPARISON OF SEGMENTATION METHODS FOR DIGITAL IMAGE ANALYSIS OF CONFOCAL MICROSCOPE IMAGES TO MEASURE TRACHEID CELL DIMENSIONS." IAWA Journal 22, no. 3 (2001): 267–88. http://dx.doi.org/10.1163/22941932-90000284.

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Image analysis is a common tool for measuring tracheid cell dimensions. When analyzing a digital image of a transverse cross section of wood, one of the initial procedures is that of segmentation. This involves classifying a picture element (pixel) as either cell wall or lumen. The accuracy of tracheid measurements is dependent on how well the result of the segmentation procedure corresponds to the true distributions of cell wall or lumen pixels. In this paper a comparison of segmentation methods is given. The effect of segmentation method on measurements is investigated and the performance of

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Wang, Yuliang, Tongda Lu, Xiaolai Li, Shuai Ren, and Shusheng Bi. "Robust nanobubble and nanodroplet segmentation in atomic force microscope images using the spherical Hough transform." Beilstein Journal of Nanotechnology 8 (December 1, 2017): 2572–82. http://dx.doi.org/10.3762/bjnano.8.257.

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Interfacial nanobubbles (NBs) and nanodroplets (NDs) have been attracting increasing attention due to their potential for numerous applications. As a result, the automated segmentation and morphological characterization of NBs and NDs in atomic force microscope (AFM) images is highly awaited. The current segmentation methods suffer from the uneven background in AFM images due to thermal drift and hysteresis of AFM scanners. In this study, a two-step approach was proposed to segment NBs and NDs in AFM images in an automated manner. The spherical Hough transform (SHT) and a boundary optimization

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Pai, Chih-Yun, Hunter Morera, Sudeep Sarkar, et al. "Automated pressure ulcer dimension measurements using a depth camera." Journal of Wound Care 34, no. 3 (2025): 205–14. https://doi.org/10.12968/jowc.2021.0171.

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Objective: The purpose of this research was to develop an automatic wound segmentation method for a pressure ulcer (PU) monitoring system (PrUMS) using a depth camera to provide automated, non-contact wound measurements. Method: The automatic wound segmentation method, which combines multiple convolutional neural network classifiers, was developed to segment the wound region to improve PrUMS accuracy and to avoid the biased decision from a single classifier. Measurements from PrUMS were compared with the standardised manual measurements (ground truth) of two clinically trained wound care nurse

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Wan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, and Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net." Sensors 20, no. 16 (2020): 4473. http://dx.doi.org/10.3390/s20164473.

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Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which

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Mukondiwa, Daisy Thembelihle, YongTao Shi, and Chao Gao. "A Prostate Boundary Localization and Edge Denoising Algorithm." East African Journal of Information Technology 7, no. 1 (2024): 108–20. http://dx.doi.org/10.37284/eajit.7.1.1900.

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This research aimed at presenting a two-step method for prostate segmentation in TRUS images. The research used a prostate boundary localization and prostate edge denoising approach. The proposed method contribution is the use of the optimized Hodge’s method as the boundary operator and the use of the Bidirectional Exponential moving average to perform edge denoising. The results showed that the proposed method is effective in completing the prostate segmentation task. (1) The prostate region is effectively initialized and localized. (2) The recovery of noise points is accomplished and the seg

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Kumar, S. Pramod, and Mrityunjaya V. Latte. "Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy." Journal of Intelligent Systems 28, no. 2 (2019): 275–89. http://dx.doi.org/10.1515/jisys-2017-0020.

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Abstract The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional (2D) optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomograp

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Arafati, Arghavan, Daisuke Morisawa, Michael R. Avendi, et al. "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 (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 probl

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Yu, Zechen, Zhongping Chen, Yang Yu, Haichen Zhu, Dan Tong, and Yang Chen. "An automated ASPECTS method with atlas-based segmentation." Computer Methods and Programs in Biomedicine 210 (October 2021): 106376. http://dx.doi.org/10.1016/j.cmpb.2021.106376.

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Weiwei, Xing, Wang Weiqiang, Bao Peng, Sun Liya, and Tong Leiming. "A novel method for automated human behavior segmentation." Computer Animation and Virtual Worlds 27, no. 5 (2016): 501–14. http://dx.doi.org/10.1002/cav.1690.

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Zemborain, Zane Zenon, Matias Soifer, Nadim S. Azar, et al. "Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus With Accuracy on Par With Human Segmentation." Cornea 42, no. 10 (2023): 1309–19. http://dx.doi.org/10.1097/ico.0000000000003319.

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Purpose: The aim of this study was to perform automated segmentation of corneal nerves and other structures in corneal confocal microscopy (CCM) images of the subbasal nerve plexus (SNP) in eyes with ocular surface diseases (OSDs). Methods: A deep learning–based 2-stage algorithm was designed to perform segmentation of SNP features. In the first stage, to address applanation artifacts, a generative adversarial network–enabled deep network was constructed to identify 3 neighboring corneal layers on each CCM image: epithelium, SNP, and stroma. This network was trained/validated on 470 images of

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Kazerooni, Anahita Fathi, Nastaran Khalili, Debanjan Haldar, et al. "IMG-05. A MULTI-INSTITUTIONAL AND MULTI-HISTOLOGY PEDIATRIC-SPECIFIC BRAIN TUMOR SUBREGION SEGMENTATION TOOL: FACILITATING RAPNO-BASED ASSESSMENT OF TREATMENT RESPONSE." Neuro-Oncology 25, Supplement_1 (2023): i47. http://dx.doi.org/10.1093/neuonc/noad073.182.

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Abstract Current response assessment in pediatric brain tumors (PBTs), as recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, relies on 2D measurements of changes in tumor size. However, there is growing evidence of underestimation of tumor size in PBTs using 2D compared to volumetric (3D) measurement approach. Accordingly, automated methods that reduce manual burden and intra- and inter-rater variability in segmenting tumor subregions and volumetric evaluations are warranted to facilitate tumor response assessment of PBTs. We have developed a fully automa

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Iglesias-Rey, Sara, Felipe Antunes-Santos, Cathleen Hagemann, et al. "Unsupervised Cell Segmentation and Labelling in Neural Tissue Images." Applied Sciences 11, no. 9 (2021): 3733. http://dx.doi.org/10.3390/app11093733.

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Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they

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Qiu, Bingjiang, Jiapan Guo, Joep Kraeima, et al. "Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography." Journal of Personalized Medicine 11, no. 6 (2021): 492. http://dx.doi.org/10.3390/jpm11060492.

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Purpose: Classic encoder–decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. The main reason is that EDCNN approaches ignore the anatomical connectivity of the organs. In this paper, we propose a novel CNN-based 3D mandible segmentation approach that has the ability to accurately segment detailed anatomical structures. Methods: Different from the classic EDCNNs that need to sl

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P, Mathumetha, Sivakumar Rajagopal, Shailly Vaidya, and Basim Alhadidi. "Automated Detection of Pneumothorax Using Frontal Chest X-rays." ECS Transactions 107, no. 1 (2022): 861–72. http://dx.doi.org/10.1149/10701.0861ecst.

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Pneumothorax detection can assist doctors in treatment and diagnosis of diseases more accurately. In this paper automated method employed image segmentation techniques for the detection. The preprocessing methods are handled by image processing techniques using MATLAB 2020b software Support Vector machine is applied to classify normal and abnormal lung chest X-ray. Features are extracted from lung image with the texture based segmentation techniques. The rib boundaries are identified with sobel edge detection. The gray level incurrence matrices segmentation method increase accuracy rate with A

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Agnihotri, Aditya. "An Efficient and Clinical-Oriented 3D Liver Segmentation Method." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (2019): 1015–21. http://dx.doi.org/10.17762/turcomat.v10i2.13584.

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Due to the vast variety of human differences in the morphologies of the liver and the variance in pixel intensity in the picture, automatic liver segmentation is challenging. Furthermore, the limits of the liver are unclear since it shares intensity distributions with neighbouring organs and tissues. We suggest a quick and accurate approach for segmenting the liver using contrast-enhanced computed tomography (CT) images in this methodology. We apply level-set speed photos to adopt the two-step seeded region growth (SRG) method to generate an initial liver boundary that is roughly defined. Acco

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Arsenescu, Tudor, Radu Chifor, Tiberiu Marita, et al. "3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation—Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography." Sensors 23, no. 5 (2023): 2806. http://dx.doi.org/10.3390/s23052806.

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The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization

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Chen, Junjie, Qian Su, Yunbin Niu, Zongyu Zhang, and Jinghao Liu. "A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction." Remote Sensing 15, no. 18 (2023): 4504. http://dx.doi.org/10.3390/rs15184504.

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To ensure efficient railroad operation and maintenance management, the accurate reconstruction of railroad BIM models is a crucial step. This paper proposes a workflow for automated segmentation and reconstruction of railroad structures using point cloud data, without relying on intensity or trajectory information. The workflow consists of four main components: point cloud adaptive denoising, scene segmentation, structure segmentation combined with deep learning, and model reconstruction. The proposed workflow was validated using two datasets with significant differences in railroad line point

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Rampun, Andrik, Philip J. Morrow, Bryan W. Scotney, and John Winder. "Fully automated breast boundary and pectoral muscle segmentation in mammograms." Artificial Intelligence in Medicine Vol. 79 (June 9, 2017): 28–41. https://doi.org/10.1016/j.artmed.2017.06.001.

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Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in Computer Aided Diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to sear

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Ye, Guochang, and Mehmet Kaya. "Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data." Bioengineering 9, no. 2 (2022): 81. http://dx.doi.org/10.3390/bioengineering9020081.

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Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically achieve cell segmentation for phase-contrast microscopes. Manual/visual counting of cell segmentation serves as the control group (156 images as ground truth) to evaluate the proposed method’s performance. The proposed technology’s adaptive performance is assessed at varying condit

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Mihelic, Samuel A., William A. Sikora, Ahmed M. Hassan, Michael R. Williamson, Theresa A. Jones, and Andrew K. Dunn. "Segmentation-Less, Automated, Vascular Vectorization." PLOS Computational Biology 17, no. 10 (2021): e1009451. http://dx.doi.org/10.1371/journal.pcbi.1009451.

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Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (bina

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