{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:35:31Z","timestamp":1773221731777,"version":"3.50.1"},"reference-count":244,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"am","delay-in-days":339,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100009023","name":"Precursory Research for Embryonic Science and Technology","doi-asserted-by":"publisher","award":["JPMJPR23P7"],"award-info":[{"award-number":["JPMJPR23P7"]}],"id":[{"id":"10.13039\/501100009023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.compbiomed.2025.111171","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T15:08:24Z","timestamp":1759763304000},"page":"111171","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"PA","title":["Advances in medical image segmentation: A comprehensive survey with a focus on lumbar spine applications"],"prefix":"10.1016","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-8185","authenticated-orcid":false,"given":"Ahmed","family":"Kabil","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7332-0759","authenticated-orcid":false,"given":"Ghada","family":"Khoriba","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0316-2724","authenticated-orcid":false,"given":"Mina","family":"Yousef","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-9807","authenticated-orcid":false,"given":"Essam A.","family":"Rashed","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b1","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/5164970","article-title":"Modern diagnostic imaging technique applications and risk factors in the medical field: A review","volume":"2022","author":"Hussain","year":"2022","journal-title":"BioMed Res. Int."},{"key":"10.1016\/j.compbiomed.2025.111171_b2","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.439","article-title":"Image thresholding approaches for medical image segmentation-short literature review","volume":"219","author":"Jardim","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compbiomed.2025.111171_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102360","article-title":"Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review","volume":"77","author":"Li","year":"2022","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b4","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/7091301","article-title":"A review paper about deep learning for medical image analysis","volume":"2023","author":"Sistaninejhad","year":"2023","journal-title":"Comput. Math. Methods Med."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111171_b5","doi-asserted-by":"crossref","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical image segmentation based on U-Net: A review","volume":"64","author":"Du","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"10.1016\/j.compbiomed.2025.111171_b6","series-title":"Adaptive medical image segmentation using deep convolutional neural networks","first-page":"15","author":"Babu","year":"2023"},{"issue":"27","key":"10.1016\/j.compbiomed.2025.111171_b7","article-title":"A review of medical image segmentation algorithms","volume":"7","author":"Ramesh","year":"2021","journal-title":"EAI Endorsed Trans. Pervasive Heal. Technol."},{"key":"10.1016\/j.compbiomed.2025.111171_b8","series-title":"Data Driven Approaches on Medical Imaging","first-page":"1","article-title":"Introduction of medical imaging modalities","author":"Islam","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b9","series-title":"Principles of Medical Imaging","author":"Shung","year":"2012"},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111171_b10","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1049\/ipr2.12419","article-title":"Medical image segmentation using deep learning: A survey","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Process."},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111171_b11","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering11101034","article-title":"Advances in medical image segmentation: A comprehensive review of traditional, deep learning and hybrid approaches","volume":"11","author":"Xu","year":"2024","journal-title":"Bioengineering"},{"key":"10.1016\/j.compbiomed.2025.111171_b12","article-title":"Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation","author":"Jiao","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111171_b13","article-title":"Application of U-Net and optimized clustering in medical image segmentation: A review","volume":"136","author":"Shao","year":"2023","journal-title":"CMES Comput. Model. Eng. Sci."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111171_b14","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TRPMS.2023.3265863","article-title":"Current and emerging trends in medical image segmentation with deep learning","volume":"7","author":"Conze","year":"2023","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10.1016\/j.compbiomed.2025.111171_b15","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111171_b16","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.3390\/su13031224","article-title":"A review of deep-learning-based medical image segmentation methods","volume":"13","author":"Liu","year":"2021","journal-title":"Sustainability"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","article-title":"Deep learning models for digital image processing: a review","volume":"57","author":"Archana","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compbiomed.2025.111171_b18","series-title":"Machine Vision: Theory, Algorithms, Practicalities","author":"Davies","year":"2004"},{"issue":"285\u2013296","key":"10.1016\/j.compbiomed.2025.111171_b19","first-page":"23","article-title":"A threshold selection method from gray-level histograms","volume":"11","author":"Otsu","year":"1975","journal-title":"Automatica"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b20","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0031-3203(86)90030-0","article-title":"Minimum error thresholding","volume":"19","author":"Kittler","year":"1986","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111171_b21","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A new method for gray-level picture thresholding using the entropy of the histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"10.1016\/j.compbiomed.2025.111171_b22","series-title":"Handbook of Medical Imaging, Processing and Analysis","first-page":"69","article-title":"Overview and fundamentals of medical image segmentation","author":"Rogowska","year":"2000"},{"key":"10.1016\/j.compbiomed.2025.111171_b23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10444-020-09838-3","article-title":"Edge detection with trigonometric polynomial shearlets","volume":"47","author":"Schober","year":"2021","journal-title":"Adv. Comput. Math."},{"key":"10.1016\/j.compbiomed.2025.111171_b24","series-title":"2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research","first-page":"1","article-title":"Segmentation of brain tumor in MRI images using CNN with edge detection","author":"Archa","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b25","series-title":"2018 IEEE 3rd International Conference on Image, Vision and Computing","first-page":"273","article-title":"Automatic intestinal canal Segmentation Based Region growing with multi-scale entropy","author":"Hua","year":"2018"},{"issue":"9","key":"10.1016\/j.compbiomed.2025.111171_b26","doi-asserted-by":"crossref","first-page":"10099","DOI":"10.1007\/s10462-023-10426-2","article-title":"ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition","volume":"56","author":"Ranjbarzadeh","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.compbiomed.2025.111171_b27","series-title":"Algorithms for Graphics and Image Processing","author":"Pavlidis","year":"2012"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b28","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TCBB.2019.2963873","article-title":"A novel negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space and its application to brain CT image segmentation","volume":"18","author":"Jiang","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biology Bioinform."},{"key":"10.1016\/j.compbiomed.2025.111171_b29","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s13748-021-00251-5","article-title":"Enhanced football game optimization-based K-means clustering for multi-level segmentation of medical images","volume":"10","author":"Abhiraj","year":"2021","journal-title":"Prog. Artif. Intell."},{"key":"10.1016\/j.compbiomed.2025.111171_b30","first-page":"173","article-title":"Segmentation of brain lesions in MRI and CT scan images: a hybrid approach using k-means clustering and image morphology","volume":"99","author":"Agrawal","year":"2018","journal-title":"J. Inst. Eng. (India): Ser. B"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b31","article-title":"The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data","volume":"978","author":"Husein","year":"2018","journal-title":"J. Phys.: Conf. Ser."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111171_b32","doi-asserted-by":"crossref","first-page":"6939","DOI":"10.1007\/s11042-020-09635-6","article-title":"An improved gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation","volume":"80","author":"Kumar","year":"2021","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compbiomed.2025.111171_b33","series-title":"2019 6th International Conference on Image and Signal Processing and their Applications","first-page":"1","article-title":"An improved clustering method based on K-Means algorithm for MRI brain tumor segmentation","author":"Mehidi","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b34","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/RBME.2018.2798701","article-title":"A survey of graph cuts\/graph search based medical image segmentation","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111171_b35","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabCut\u201d interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111171_b36","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1007\/s11042-023-15472-0","article-title":"Image segmentation using a novel dual active contour model","volume":"83","author":"Fang","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compbiomed.2025.111171_b37","series-title":"BMVC92: Proceedings of the British Machine Vision Conference, Organised By the British Machine Vision Association 22\u201324 September 1992 Leeds","first-page":"266","article-title":"Active shape models\u2014\u2018smart snakes\u2019","author":"Cootes","year":"1992"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111171_b38","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/34.927467","article-title":"Active appearance models","volume":"23","author":"Cootes","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b39","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/0021-9991(88)90002-2","article-title":"Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations","volume":"79","author":"Osher","year":"1988","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.compbiomed.2025.111171_b40","doi-asserted-by":"crossref","first-page":"95253","DOI":"10.1109\/ACCESS.2023.3309158","article-title":"EMED-unet: An efficient multi-encoder-decoder based unet for medical image segmentation","volume":"11","author":"Shah","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111171_b41","article-title":"Medical image segmentation based on U-net","volume":"2547","author":"Chen","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b42","first-page":"2970","article-title":"Brain tumour detection using deep learning: A review","volume":"vol. 2","author":"Kavyasri","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b43","doi-asserted-by":"crossref","DOI":"10.1007\/s11042-024-19920-3","article-title":"A comprehensive review on artificial intelligence-driven preprocessing, segmentation, and classification techniques for precision furcation analysis in radiographic images","author":"Juneja","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compbiomed.2025.111171_b44","series-title":"2021 10th International Conference on Information and Automation for Sustainability","first-page":"185","article-title":"The effect of deep learning and machine learning approaches for brain tumor recognition","author":"Arumaiththurai","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b45","series-title":"7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023 - Proceedings","first-page":"809","article-title":"Advancements in brain tumor detection using machine learning applications from MRI image analysis","author":"Sneha","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2019.108774","article-title":"Artificial intelligence applications for thoracic imaging","volume":"123","author":"Chassagnon","year":"2020","journal-title":"Eur. J. Radiol."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b47","doi-asserted-by":"crossref","DOI":"10.1002\/ima.23023","article-title":"Two-dimensional medical image segmentation based on U-shaped structure","volume":"34","author":"Cai","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.compbiomed.2025.111171_b48","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/1645479","article-title":"Medical image segmentation algorithm based on optimized convolutional neural network-adaptive dropout depth calculation","volume":"2020","author":"An","year":"2020","journal-title":"Complexity"},{"key":"10.1016\/j.compbiomed.2025.111171_b49","series-title":"DC-UNet: Rethinking the U-net architecture with dual channel efficient CNN for medical image segmentation","author":"Lou","year":"2021"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e41409","article-title":"Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors","volume":"11","author":"Nafchi","year":"2025","journal-title":"Heliyon"},{"key":"10.1016\/j.compbiomed.2025.111171_b51","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111171_b52","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"10.1016\/j.compbiomed.2025.111171_b53","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compbiomed.2025.111171_b54","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.compbiomed.2025.111171_b55","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"5534","article-title":"Learning spatio-temporal representation with pseudo-3D residual networks","author":"Qiu","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b56","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b57","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","article-title":"USE-Net: Incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets","volume":"365","author":"Rundo","year":"2019","journal-title":"Neurocomputing"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b58","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.compbiomed.2025.111171_b59","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compbiomed.2025.111171_b60","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2881","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b61","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b62","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b63","series-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"Chen","year":"2014"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b64","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111171_b65","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1002\/mp.12480","article-title":"Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method","volume":"44","author":"Zhou","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.compbiomed.2025.111171_b66","series-title":"Focal fcn: Towards small object segmentation with limited training data","author":"Zhou","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b67","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neunet.2020.03.007","article-title":"AdaEn-Net: An ensemble of adaptive 2D\u20133D fully convolutional networks for medical image segmentation","volume":"126","author":"Baldeon Calisto","year":"2020","journal-title":"Neural Netw."},{"key":"10.1016\/j.compbiomed.2025.111171_b68","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"571","article-title":"Collaborative multi-view convolutions with gating for accurate and fast volumetric medical image segmentation","author":"Li","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b69","series-title":"2022 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"1748","article-title":"UNETR: Transformers for 3D medical image segmentation","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b70","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b71","series-title":"2019 18th IEEE International Conference on Machine Learning and Applications","first-page":"1725","article-title":"Knee bone segmentation on three-dimensional MRI","author":"Almajalid","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b72","first-page":"221","article-title":"Automatically segmenting the left atrium from cardiac images using successive 3D U-nets and a contour loss","volume":"vol. 11395 LNCS","author":"Jia","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b73","series-title":"2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference","first-page":"208","article-title":"Segmentation of liver and its tumor based on U-net","volume":"5","author":"Chai","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b74","article-title":"A novel brain image segmentation method using an improved 3D U-net model","volume":"2021","author":"Yang","year":"2021","journal-title":"Sci. Program."},{"key":"10.1016\/j.compbiomed.2025.111171_b75","series-title":"2024 2nd International Conference on Signal Processing and Intelligent Computing","first-page":"881","article-title":"Improved medical image segmentation method and three-dimensional reconstruction based on 3D-unet","author":"Guo","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b76","first-page":"428","article-title":"Dilated convolutions based 3D U-net for multi-modal brain image segmentation","volume":"vol. 413 LNNS","author":"Kemassi","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b77","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2022.911679","article-title":"Half-UNet: A simplified U-net architecture for medical image segmentation","volume":"16","author":"Lu","year":"2022","journal-title":"Front. Neuroinformatics"},{"issue":"09","key":"10.1016\/j.compbiomed.2025.111171_b78","doi-asserted-by":"crossref","DOI":"10.1142\/S0219519423401024","article-title":"A novel cardiac image segmentation method using an optimized 3D U-Net model","volume":"23","author":"Dong","year":"2023","journal-title":"J. Mech. Med. Biology"},{"key":"10.1016\/j.compbiomed.2025.111171_b79","series-title":"Attention U-Net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b80","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b81","series-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3367","article-title":"Recurrent convolutional neural network for object recognition","author":"Liang","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b82","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b83","series-title":"Image Analysis and Recognition: 16th International Conference, ICIAR 2019, Waterloo, on, Canada, August 27\u201329, 2019, Proceedings, Part II 16","first-page":"106","article-title":"TPUAR-Net: Two parallel U-net with asymmetric residual-based deep convolutional neural network for brain tumor segmentation","author":"Abd-Ellah","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b84","series-title":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1384","article-title":"Acu-Net: A 3D attention context U-Net for multiple sclerosis lesion segmentation","author":"hu","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b85","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s11760-020-01835-9","article-title":"MSN-Net: A multi-scale context nested U-Net for liver segmentation","volume":"15","author":"Fan","year":"2021","journal-title":"Signal, Image Video Process."},{"key":"10.1016\/j.compbiomed.2025.111171_b86","series-title":"2019 IEEE International Ultrasonics Symposium","first-page":"1160","article-title":"RU-Net: A refining segmentation network for 2D echocardiography","author":"Leclerc","year":"2019"},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111171_b87","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.compbiomed.2025.111171_b88","series-title":"Proceedings of the 6th International Conference on Communication and Information Processing","first-page":"89","article-title":"A content-driven architecture for medical image segmentation","author":"Sabrowsky-Hirsch","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b89","first-page":"535","article-title":"Efficient Bayesian uncertainty estimation for nnu-net","volume":"vol. 13438 LNCS","author":"Zhao","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b90","first-page":"2171","article-title":"Reference-guided pseudo-label generation for medical semantic segmentation","volume":"vol. 36","author":"Seibold","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b91","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"vol. 3","author":"Lee","year":"2013"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b92","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1109\/JBHI.2022.3167384","article-title":"An effective semi-supervised approach for liver CT image segmentation","volume":"26","author":"Han","year":"2022","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"10.1016\/j.compbiomed.2025.111171_b93","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24","first-page":"439","article-title":"Neighbor matching for semi-supervised learning","author":"Wang","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b94","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","volume":"29","author":"Sajjadi","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b95","series-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.111171_b96","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b97","series-title":"Proceedings of the Eleventh Annual Conference on Computational Learning Theory","first-page":"92","article-title":"Combining labeled and unlabeled data with co-training","author":"Blum","year":"1998"},{"key":"10.1016\/j.compbiomed.2025.111171_b98","series-title":"International Conference on Medical Imaging with Deep Learning","first-page":"820","article-title":"Semi-supervised medical image segmentation via cross teaching between CNN and transformer","author":"Luo","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b99","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102364","article-title":"Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network","volume":"132","author":"Boutillon","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.compbiomed.2025.111171_b100","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"203","article-title":"Learning and incorporating shape models for semantic segmentation","author":"Ravishankar","year":"2017"},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111171_b101","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","article-title":"Anatomically constrained neural networks (ACNNs): Application to cardiac image enhancement and segmentation","volume":"37","author":"Oktay","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b102","first-page":"1","article-title":"Generative adversarial nets","volume":"vol. 27","author":"Goodfellow","year":"2014"},{"key":"10.1016\/j.compbiomed.2025.111171_b103","series-title":"Semantic segmentation using adversarial networks","author":"Luc","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.111171_b104","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","article-title":"Segan: Adversarial network with multi-scale L1 loss for medical image segmentation","volume":"16","author":"Xue","year":"2018","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.compbiomed.2025.111171_b105","series-title":"International Workshop on Deep Learning in Medical Image Analysis","first-page":"263","article-title":"SCAN: Structure correcting adversarial network for organ segmentation in chest X-rays","author":"Dai","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b106","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22","first-page":"68","article-title":"PAN: Projective adversarial network for medical image segmentation","author":"Khosravan","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b107","series-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"13853","article-title":"Synthetic learning: Learn from distributed asynchronized discriminator GAN without sharing medical image data","author":"Chang","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b108","series-title":"Proceedings of the IEEE International Conference on Computer Vision Workshops","first-page":"64","article-title":"Towards virtual h&e staining of hyperspectral lung histology images using conditional generative adversarial networks","author":"Bayramoglu","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b109","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112855","article-title":"Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network","volume":"139","author":"Singh","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2025.111171_b110","series-title":"Synthetic medical images from dual generative adversarial networks","author":"Guibas","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b111","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11","first-page":"720","article-title":"Craniomaxillofacial bony structures segmentation from MRI with deep-supervision adversarial learning","author":"Zhao","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b112","series-title":"Few-shot 3D multi-modal medical image segmentation using generative adversarial learning","author":"Mondal","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b113","series-title":"Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20","first-page":"408","article-title":"Deep adversarial networks for biomedical image segmentation utilizing unannotated images","author":"Zhang","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b114","series-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014"},{"key":"10.1016\/j.compbiomed.2025.111171_b115","article-title":"Rethinking semi-supervised medical image segmentation: A variance-reduction perspective","volume":"36","author":"You","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b116","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112749","article-title":"Own-background contrastive learning guided by pseudo-label for semi-supervised medical image segmentation","volume":"171","author":"Fan","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.compbiomed.2025.111171_b117","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24","first-page":"481","article-title":"Semi-supervised contrastive learning for label-efficient medical image segmentation","author":"Hu","year":"2021"},{"issue":"2","key":"10.1016\/j.compbiomed.2025.111171_b118","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.10.2.024007","article-title":"Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging","volume":"10","author":"Hooper","year":"2023","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b119","series-title":"2022 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"1769","article-title":"Semi-supervised semantic segmentation of vessel images using leaking perturbations","author":"Hou","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b120","first-page":"21786","article-title":"Not all unlabeled data are equal: Learning to weight data in semi-supervised learning","volume":"33","author":"Ren","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b121","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106419","article-title":"Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net","volume":"211","author":"Zhang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compbiomed.2025.111171_b122","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11656","article-title":"Cyclemix: A holistic strategy for medical image segmentation from scribble supervision","author":"Zhang","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b123","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24","first-page":"3","article-title":"Noisy labels are treasure: mean-teacher-assisted confident learning for hepatic vessel segmentation","author":"Xu","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b124","series-title":"2023 IEEE\/CVF International Conference on Computer Vision","first-page":"3992","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b125","series-title":"2017 IEEE International Conference on Computer Vision","first-page":"2980","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b126","series-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b127","series-title":"Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1","first-page":"37","article-title":"Squeeze-and-excitation normalization for automated delineation of head and neck primary tumors in combined PET and CT images","author":"Iantsen","year":"2021"},{"issue":"23","key":"10.1016\/j.compbiomed.2025.111171_b128","doi-asserted-by":"crossref","DOI":"10.3390\/s24237576","article-title":"Segmentation of low-grade brain tumors using mutual attention multimodal MRI","volume":"24","author":"Seshimo","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2025.111171_b129","first-page":"6315","article-title":"Non-local u-nets for biomedical image segmentation","volume":"vol. 34","author":"Wang","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b130","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b131","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105834","article-title":"HCA-former: Hybrid convolution attention transformer for 3D medical image segmentation","volume":"90","author":"Yang","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compbiomed.2025.111171_b132","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2024.104883","article-title":"Cross-scale informative priors network for medical image segmentation","volume":"157","author":"Sui","year":"2025","journal-title":"Digit. Signal Process.: Rev. J."},{"key":"10.1016\/j.compbiomed.2025.111171_b133","series-title":"International MICCAI Brainlesion Workshop","first-page":"272","article-title":"Swin UNETR: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b134","article-title":"Polyp-PVT: Polyp segmentation with pyramid vision transformers","volume":"2","author":"Dong","year":"2023","journal-title":"CAAI Artif. Intell. Res."},{"key":"10.1016\/j.compbiomed.2025.111171_b135","series-title":"Characterizing renal structures with 3D block aggregate transformers","author":"Yu","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b136","series-title":"TransUNet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.compbiomed.2025.111171_b137","series-title":"European Conference on Computer Vision","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b138","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"574","article-title":"UNETR: Transformers for 3D medical image segmentation","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b139","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"NAS-Unet: Neural architecture search for medical image segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111171_b140","article-title":"Bayesian optimization with unknown search space","volume":"32","author":"Ha","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b141","series-title":"Meta-learning: A survey","author":"Vanschoren","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b142","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2020.104042","article-title":"Deep multimodal fusion for semantic image segmentation: A survey","volume":"105","author":"Zhang","year":"2021","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.compbiomed.2025.111171_b143","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2223","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b144","series-title":"Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings 25","first-page":"597","article-title":"Unsupervised domain adaptation in brain lesion segmentation with adversarial networks","author":"Kamnitsas","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b145","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"476","article-title":"A lifelong learning approach to brain MR segmentation across scanners and protocols","author":"Karani","year":"2018"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111171_b146","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1109\/TMI.2019.2963882","article-title":"Unpaired multi-modal segmentation via knowledge distillation","volume":"39","author":"Dou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.111171_b147","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.compbiomed.2025.111171_b148","series-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b149","series-title":"2022 IEEE 19th International Symposium on Biomedical Imaging","first-page":"1","article-title":"Unsupervised ensemble distillation for multi-organ segmentation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.111171_b150","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101693","article-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","volume":"63","author":"Tajbakhsh","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b151","series-title":"An overview of multi-task learning in deep neural networks","author":"Ruder","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b152","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11","first-page":"101","article-title":"A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images","author":"Playout","year":"2018"},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111171_b153","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TMI.2020.3048055","article-title":"Diminishing uncertainty within the training pool: Active learning for medical image segmentation","volume":"40","author":"Nath","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b154","first-page":"48","article-title":"M-VAAL: Multimodal variational adversarial active learning for downstream medical image analysis tasks","volume":"vol. 14122 LNCS","author":"Khanal","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b155","first-page":"20","article-title":"Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs","volume":"vol. 11435 LNCS","author":"Carse","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b156","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102062","article-title":"A survey on active learning and human-in-the-loop deep learning for medical image analysis","volume":"71","author":"Budd","year":"2021","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b157","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1038\/s41467-020-17478-w","article-title":"Causality matters in medical imaging","volume":"11","author":"Castro","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compbiomed.2025.111171_b158","unstructured":"P. Conde, C. Premebida, Adaptive-TTA: accuracy-consistent weighted test time augmentation method for the uncertainty calibration of deep learning classifiers, in: BMVC 2022 - 33rd British Machine Vision Conference Proceedings, 2022."},{"key":"10.1016\/j.compbiomed.2025.111171_b159","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102616","article-title":"Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge","volume":"82","author":"Ma","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b160","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neucom.2021.01.135","article-title":"Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss","volume":"442","author":"Lei","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compbiomed.2025.111171_b161","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"184","article-title":"3D dilated multi-fiber network for real-time brain tumor segmentation in MRI","author":"Chen","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b162","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging","first-page":"442","article-title":"V-net light-parameter-efficient 3-D convolutional neural network for prostate MRI segmentation","author":"Yaniv","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b163","series-title":"MRI three-dimensional reconstruction of liver and tumor based on deep learning","first-page":"451","author":"Qin","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b164","article-title":"MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways","author":"Dai","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111171_b165","doi-asserted-by":"crossref","DOI":"10.3390\/s25051513","article-title":"Lightweight explicit 3D human digitization via normal integration","volume":"25","author":"Liu","year":"2025","journal-title":"Sensors"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.111171_b166","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13996","article-title":"Lumbar spine segmentation method based on deep learning","volume":"24","author":"Lu","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.compbiomed.2025.111171_b167","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125862","article-title":"Machine-agnostic automated lumbar MRI segmentation using a cascaded model based on generative neurons","volume":"264","author":"Basak","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2025.111171_b168","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105794","article-title":"Multi-head consistent semi-supervised learning for lumbar CT segmentation","volume":"90","author":"He","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b169","doi-asserted-by":"crossref","DOI":"10.3390\/s22041547","article-title":"Localization and edge-based segmentation of lumbar spine vertebrae to identify the deformities using deep learning models","volume":"22","author":"Mushtaq","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2025.111171_b170","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105833","article-title":"Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation","volume":"200","author":"Kim","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compbiomed.2025.111171_b171","doi-asserted-by":"crossref","first-page":"77999","DOI":"10.1109\/ACCESS.2024.3407833","article-title":"Multi-scale hybrid attention convolutional neural network for automatic segmentation of lumbar vertebrae from MRI","volume":"12","author":"Liu","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111171_b172","series-title":"2020 6th International Conference on Advanced Computing and Communication Systems","first-page":"945","article-title":"Spine magnetic resonance image segmentation using deep learning techniques","author":"Andrew","year":"2020"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b173","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/2345835","article-title":"CT-based automatic spine segmentation using patch-based deep learning","volume":"2023","author":"Qadri","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.compbiomed.2025.111171_b174","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.future.2018.03.023","article-title":"Automated system for the detection of thoracolumbar fractures using a CNN architecture","volume":"85","author":"Raghavendra","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b175","first-page":"350","article-title":"Lumbar spinal stenosis: diagnosis and management","volume":"109","author":"Webb","year":"2024","journal-title":"Am. Fam. Physician"},{"issue":"1s5","key":"10.1016\/j.compbiomed.2025.111171_b176","first-page":"V","article-title":"Aortic stenosis","volume":"38","author":"Ross, Jr.","year":"1968","journal-title":"Circulation"},{"key":"10.1016\/j.compbiomed.2025.111171_b177","series-title":"2012 9th IEEE International Symposium on Biomedical Imaging","first-page":"114","article-title":"A new approach to automatic disc localization in clinical lumbar MRI: Combining machine learning with heuristics","author":"Ghosh","year":"2012"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b178","article-title":"Scoliosis: Review of diagnosis and treatment","volume":"4","author":"Kumar","year":"2024","journal-title":"Int. J. Converg. Heal."},{"key":"10.1016\/j.compbiomed.2025.111171_b179","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s00586-005-1053-9","article-title":"The adult scoliosis","volume":"14","author":"Aebi","year":"2005","journal-title":"Eur. Spine J."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b180","article-title":"Cobb angle measurement of spine from X-ray images using convolutional neural network","volume":"2019","author":"Horng","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.compbiomed.2025.111171_b181","first-page":"1127","article-title":"Osteoporotic fractures: diagnosis, evaluation, and significance from the international working group on DXA best practices","volume":"vol. 99","author":"Khan","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b182","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1007\/s00198-004-1702-6","article-title":"Epidemiology of osteoporotic fractures","volume":"16","author":"Johnell","year":"2005","journal-title":"Osteoporos Int."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b183","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10143-024-02553-3","article-title":"Thoracolumbar fracture and spinal cord injury in blunt trauma: a systematic review, meta-analysis, and meta-regression","volume":"47","author":"Azizi","year":"2024","journal-title":"Neurosurg. Rev."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b184","doi-asserted-by":"crossref","first-page":"461","DOI":"10.2106\/00004623-198365040-00006","article-title":"The value of computed tomography in thoracolumbar fractures. An analysis of one hundred consecutive cases and a new classification","volume":"65","author":"McAfee","year":"1983","journal-title":"J. Bone Jt. Surg."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.111171_b185","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/s41584-022-00888-z","article-title":"Intervertebral disc degeneration and osteoarthritis: a common molecular disease spectrum","volume":"19","author":"Fine","year":"2023","journal-title":"Nat. Rev. Rheumatol."},{"issue":"9827","key":"10.1016\/j.compbiomed.2025.111171_b186","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1016\/S0140-6736(12)60282-7","article-title":"Age-related macular degeneration","volume":"379","author":"Lim","year":"2012","journal-title":"Lancet"},{"key":"10.1016\/j.compbiomed.2025.111171_b187","first-page":"18","article-title":"Detection of degenerative change in lateral projection cervical spine X-ray images","volume":"vol. 9414","author":"Jebri","year":"2015"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b188","doi-asserted-by":"crossref","first-page":"626","DOI":"10.3947\/ic.2020.52.4.626","article-title":"Application of simultaneous 18F-FDG PET\/MRI for evaluating residual lesion in pyogenic spine infection: A case report","volume":"52","author":"Jeon","year":"2020","journal-title":"Infect. Chemother."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111171_b189","doi-asserted-by":"crossref","first-page":"E187","DOI":"10.1097\/BRS.0000000000004159","article-title":"An objective assessment of lumbar spine degeneration\/ageing seen on MRI using an ensemble method\u2014A novel approach to lumbar MRI reporting","volume":"47","author":"Sneath","year":"2022","journal-title":"Spine"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b190","article-title":"Pelvic incidence: A predictive factor for three-dimensional acetabular orientation\u2014A preliminary study","volume":"2014","author":"Boulay","year":"2014","journal-title":"Anat. Res. Int."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b191","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1007\/s10278-017-9945-x","article-title":"Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data","volume":"30","author":"Forsberg","year":"2017","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b192","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/app9010069","article-title":"Automatic deep feature learning via patch-based deep belief network for vertebrae segmentation in CT images","volume":"9","author":"Furqan Qadri","year":"2018","journal-title":"Appl. Sci."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.111171_b193","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b194","series-title":"Image and Graphics Technologies and Applications: 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018, Beijing, China, April 8\u201310, 2018, Revised Selected Papers 13","first-page":"536","article-title":"Deep belief network based vertebra segmentation for CT images","author":"Qadri","year":"2018"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b195","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TMI.2015.2396774","article-title":"Accurate segmentation of vertebral bodies and processes using statistical shape decomposition and conditional models","volume":"34","author":"Perea\u00f1ez","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b196","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1109\/TMI.2015.2443912","article-title":"Statistical interspace models (SIMs): application to robust 3D spine segmentation","volume":"34","author":"Castro-Mateos","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.compbiomed.2025.111171_b197","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TMI.2013.2268424","article-title":"Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model","volume":"32","author":"Rasoulian","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111171_b198","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TMI.2017.2667578","article-title":"Segmentation of pathological structures by landmark-assisted deformable models","volume":"36","author":"Ibragimov","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b199","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/TMI.2013.2296976","article-title":"Shape representation for efficient landmark-based segmentation in 3-D","volume":"33","author":"Ibragimov","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.111171_b200","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/TMI.2013.2244903","article-title":"Spine segmentation in medical images using manifold embeddings and higher-order MRFs","volume":"32","author":"Kadoury","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b201","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.media.2011.01.006","article-title":"Automatic inference of articulated spine models in CT images using high-order Markov random fields","volume":"15","author":"Kadoury","year":"2011","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b202","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compbiomed.2016.03.009","article-title":"Automatic segmentation of vertebral contours from CT images using fuzzy corners","volume":"72","author":"Athertya","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2025.111171_b203","series-title":"Recent Advances in Computational Methods and Clinical Applications for Spine Imaging","first-page":"227","article-title":"Vertebrae segmentation in 3D CT images based on a variational framework","author":"Hammernik","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b204","series-title":"Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop Held At the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan","first-page":"25","article-title":"A robust segmentation framework for spine trauma diagnosis","author":"Lim","year":"2014"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b205","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TMI.2015.2389334","article-title":"A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation","volume":"34","author":"Korez","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b206","first-page":"269","article-title":"Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images","volume":"vol. 9415","author":"Suzani","year":"2015"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.111171_b207","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0143327","article-title":"Fully automatic localization and segmentation of 3D vertebral bodies from CT\/MR images via a learning-based method","volume":"10","author":"Chu","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2025.111171_b208","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"433","article-title":"Model-based segmentation of vertebral bodies from MR images with 3D CNNs","author":"Korez","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.111171_b209","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.mri.2019.02.013","article-title":"Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI","volume":"64","author":"Lang","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b210","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.111171_b211","series-title":"2018 IEEE Congress on Evolutionary Computation","first-page":"1","article-title":"Segmentation of lumbar spine MRI images for stenosis detection using patch-based pixel classification neural network","author":"Al Kafri","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b212","doi-asserted-by":"crossref","first-page":"43487","DOI":"10.1109\/ACCESS.2019.2908002","article-title":"Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks","volume":"7","author":"Al-Kafri","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2025.111171_b213","series-title":"International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging","first-page":"108","article-title":"Attention-driven deep learning for pathological spine segmentation","author":"Sekuboyina","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b214","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging","first-page":"893","article-title":"Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks","author":"Janssens","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b215","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.media.2019.02.005","article-title":"Iterative fully convolutional neural networks for automatic vertebra segmentation and identification","volume":"53","author":"Lessmann","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b216","series-title":"3D vertebra segmentation by feature selection active shape model","first-page":"241","author":"Castro-Mateos","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b217","series-title":"Improving vertebrae segmentation using a centroid detection-guided transformer-based network","author":"Aydodgu","year":"2024"},{"key":"10.1016\/j.compbiomed.2025.111171_b218","series-title":"Segmentation of vertebral bodies in MR images","first-page":"135","author":"Zuki\u0107","year":"2012"},{"issue":"44","key":"10.1016\/j.compbiomed.2025.111171_b219","first-page":"30631","article-title":"Segmentation, diagnosis and analysis of inter-vertebral discs using lumbar spine MRI","volume":"10","author":"Sajeer","year":"2015","journal-title":"Int. J. Appl. Eng. Res."},{"key":"10.1016\/j.compbiomed.2025.111171_b220","series-title":"Research on segmentation algorithm for vertebral CT images based on spatial configuration-net and U-net deep learning model","first-page":"236","author":"Yang","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.111171_b221","series-title":"IST 2017 - IEEE International Conference on Imaging Systems and Techniques, Proceedings","first-page":"1","article-title":"3D simultaneous segmentation and registration of vertebral bodies for accurate BMD measurements","volume":"2018-January","author":"Boneta","year":"2017"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b222","doi-asserted-by":"crossref","first-page":"869","DOI":"10.3390\/signals5040047","article-title":"Identification of vertebrae in CT scans for improved clinical outcomes using advanced image segmentation","volume":"5","author":"Sushmitha","year":"2024","journal-title":"Signals"},{"key":"10.1016\/j.compbiomed.2025.111171_b223","series-title":"A fully automated level-set based segmentation method of thoracic and lumbar vertebral bodies in computed tomography images","first-page":"3049","author":"Ruiz-Espana","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b224","series-title":"Atlas-based segmentation of the thoracic and lumbar vertebrae","first-page":"215","author":"Forsberg","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b225","series-title":"5th International Conference on Image Processing, Theory, Tools and Applications 2015","first-page":"157","article-title":"2-step robust vertebra segmentation","author":"Courbot","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.111171_b226","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/978-3-319-12508-4_13","article-title":"Model-based segmentation, reconstruction and analysis of the vertebral body from spinal CT","volume":"18","author":"Aslan","year":"2015","journal-title":"Lect. Notes Comput. Vis. Biomech."},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b227","doi-asserted-by":"crossref","first-page":"3976","DOI":"10.1109\/JBHI.2022.3158968","article-title":"Attention gate based dual-pathway network for vertebra segmentation of X-Ray spine images","volume":"26","author":"Shi","year":"2022","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b228","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1097\/RCT.0b013e3181b12242","article-title":"Automated segmentation method for spinal column based on a dual elliptic column model and its application for virtual spinal straightening","volume":"34","author":"Hanaoka","year":"2010","journal-title":"J. Comput. Assist. Tomogr."},{"key":"10.1016\/j.compbiomed.2025.111171_b229","first-page":"39","article-title":"Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images","volume":"vol. 10574","author":"Lessmann","year":"2018"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.111171_b230","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.4.4.041302","article-title":"Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks","volume":"4","author":"Cheng","year":"2017","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b231","series-title":"VISIGRAPP (5: VISAPP)","first-page":"124","article-title":"Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-net and U-net","author":"Payer","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b232","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.111171_b233","series-title":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1055","article-title":"Unet 3+: A full-scale connected unet for medical image segmentation","author":"Huang","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.111171_b234","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2017.12.013","article-title":"Vertebral body segmentation in wide range clinical routine spine MRI data","volume":"155","author":"Hille","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compbiomed.2025.111171_b235","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","article-title":"Integrating spatial configuration into heatmap regression based CNNs for landmark localization","volume":"54","author":"Payer","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2025.111171_b236","series-title":"A localisation-segmentation approach for multi-label annotation of lumbar vertebrae using deep nets","author":"Sekuboyina","year":"2017"},{"key":"10.1016\/j.compbiomed.2025.111171_b237","series-title":"Universal segmentation of 33 anatomies","author":"Liu","year":"2022"},{"issue":"5786","key":"10.1016\/j.compbiomed.2025.111171_b238","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.111171_b239","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1109\/TPAMI.2012.277","article-title":"Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data","volume":"35","author":"Shin","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.111171_b240","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1038\/s41598-021-83184-2","article-title":"Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network","volume":"11","author":"Aslam","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2025.111171_b241","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.compbiomed.2018.05.027","article-title":"Ischemic stroke lesion segmentation using stacked sparse autoencoder","volume":"99","author":"Praveen","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compbiomed.2025.111171_b242","first-page":"1206","article-title":"Vertebrae segmentation via stacked sparse autoencoder from computed tomography images","volume":"vol. 11179","author":"Qadri","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.111171_b243","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s10278-018-0140-5","article-title":"Automatic vertebrae localization and identification by combining deep SSAE contextual features and structured regression forest","volume":"32","author":"Wang","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.compbiomed.2025.111171_b244","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging","first-page":"384","article-title":"Longitudinal and multi-modal data learning for parkinson\u2019s disease diagnosis via stacked sparse auto-encoder","author":"Li","year":"2019"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525015240?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525015240?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T12:17:06Z","timestamp":1773145026000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525015240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":244,"alternative-id":["S0010482525015240"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111171","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advances in medical image segmentation: A comprehensive survey with a focus on lumbar spine applications","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.111171","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111171"}}