{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:24:37Z","timestamp":1777879477418,"version":"3.51.4"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110304","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T17:56:33Z","timestamp":1776966993000},"page":"110304","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Automated microvascular segmentation in histology images using WSDetect-Net: a hybrid deep learning approach"],"prefix":"10.1016","volume":"121","author":[{"given":"Muhammadu Sathik","family":"Raja","sequence":"first","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.bspc.2026.110304_b0005","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.irbm.2022.03.001","article-title":"Fundus retinal vessels image segmentation method based on improved U-Net","volume":"43","author":"Han","year":"2022","journal-title":"IRBM"},{"issue":"8","key":"10.1016\/j.bspc.2026.110304_b0010","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s00138-016-0781-7","article-title":"Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters","volume":"27","author":"Strisciuglio","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"10.1016\/j.bspc.2026.110304_b0015","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3389\/fnins.2014.00229","article-title":"Deep learning for neuroimaging: a validation study","volume":"8","author":"Plis","year":"2014","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.bspc.2026.110304_b0020","series-title":"Proc. ICARCV","first-page":"844","article-title":"Medical image classification with convolutional neural network","author":"Li","year":"2014"},{"issue":"9","key":"10.1016\/j.bspc.2026.110304_b0025","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0137036","article-title":"Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks","volume":"10","author":"Ypsilantis","year":"2015","journal-title":"PLoS One"},{"issue":"3","key":"10.1016\/j.bspc.2026.110304_b0030","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa128","article-title":"Using deep neural networks and biological subwords to detect protein S-sulfenylation sites","volume":"22","author":"Do","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.bspc.2026.110304_b0035","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1162\/neco.2009.10-08-881","article-title":"Convolutional networks can learn to generate affinity graphs for image segmentation","volume":"22","author":"Turaga","year":"2010","journal-title":"Neural Comput."},{"key":"10.1016\/j.bspc.2026.110304_b0040","doi-asserted-by":"crossref","unstructured":"H.R. Roth, L. Lu, A. Farag, H.C. Shin, J. Liu, E.B. Turkbey, R.M. Summers, DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation, in: MICCAI 2015, Springer, 2015, pp. 556\u2013564.","DOI":"10.1007\/978-3-319-24553-9_68"},{"issue":"12","key":"10.1016\/j.bspc.2026.110304_b0045","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: a deep convolutional encoder\u2013decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110304_b0050","unstructured":"A. Kirillov, Author profile, Google Scholar, available at: https:\/\/scholar.google.com\/citations?user=bHn29ScAAAAJ (Accessed August 8, 2024)."},{"key":"10.1016\/j.bspc.2026.110304_b0055","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3389\/fninf.2019.00077","article-title":"Unsupervised cerebrovascular segmentation of TOF-MRA images based on deep neural network and hidden Markov random field model","volume":"13","author":"Fan","year":"2020","journal-title":"Front. Neuroinform."},{"key":"10.1016\/j.bspc.2026.110304_b0060","doi-asserted-by":"crossref","unstructured":"W. Wang, J. Zhong, H. Wu, Z. Wen, J. Qin, Rvseg-Net: an efficient feature pyramid cascade network for retinal vessel segmentation, in: MICCAI, Springer, 2020, pp. 796\u2013805.","DOI":"10.1007\/978-3-030-59722-1_77"},{"issue":"2","key":"10.1016\/j.bspc.2026.110304_b0065","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.bbe.2022.05.003","article-title":"Dual-channel asymmetric convolutional neural network for efficient retinal vessel segmentation","volume":"42","author":"Xu","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110304_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118313","article-title":"Width attention based convolutional neural network for retinal vessel segmentation","volume":"209","author":"Alvarado-Carrillo","year":"2022","journal-title":"Exp. Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110304_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103615","article-title":"Rethinking boundary detection in deep learning-based medical image segmentation","volume":"103","author":"Lin","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110304_b0080","series-title":"Proc. CVPR","first-page":"6596","article-title":"Dense decoder shortcut connections for single-pass semantic segmentation","author":"Bilinski","year":"2018"},{"key":"10.1016\/j.bspc.2026.110304_b0085","series-title":"Proc. CVPR","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"issue":"4","key":"10.1016\/j.bspc.2026.110304_b0090","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1049\/iet-ipr.2012.0455","article-title":"Retinal vessel segmentation by improved matched filtering","volume":"7","author":"Odstrcilik","year":"2013","journal-title":"IET Image Process."},{"key":"10.1016\/j.bspc.2026.110304_b0095","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00138-014-0636-z","article-title":"A self-adaptive matched filter for retinal blood vessel detection","volume":"26","author":"Chakraborti","year":"2015","journal-title":"Mach. Vis. Appl."},{"key":"10.1016\/j.bspc.2026.110304_b0100","doi-asserted-by":"crossref","unstructured":"A.F. Frangi, W.J. Niessen, K.L. Vincken, M.A. Viergever, Multiscale vessel enhancement filtering, in: MICCAI\u201998, Springer, 1998, pp. 130\u2013137.","DOI":"10.1007\/BFb0056195"},{"issue":"3","key":"10.1016\/j.bspc.2026.110304_b0105","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.patcog.2012.08.009","article-title":"An effective retinal blood vessel segmentation method using multi-scale line detection","volume":"46","author":"Nguyen","year":"2013","journal-title":"Pattern Recogn."},{"issue":"2","key":"10.1016\/j.bspc.2026.110304_b0110","doi-asserted-by":"crossref","first-page":"122","DOI":"10.4103\/2228-7477.130481","article-title":"Vessel segmentation in retinal images using multi-scale line operator and K-means clustering","volume":"4","author":"Saffarzadeh","year":"2014","journal-title":"J. Med. Signals Sens."},{"key":"10.1016\/j.bspc.2026.110304_b0115","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compmedimag.2015.07.006","article-title":"Retinal vessel segmentation using multi-scale textons derived from keypoints","volume":"45","author":"Zhang","year":"2015","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.bspc.2026.110304_b0120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2018.06.007","article-title":"Automatic multiscale vascular image segmentation algorithm for coronary angiography","volume":"46","author":"Carballal","year":"2018","journal-title":"Biomed. Signal Process Control"},{"issue":"22","key":"10.1016\/j.bspc.2026.110304_b0125","doi-asserted-by":"crossref","first-page":"4949","DOI":"10.3390\/s19224949","article-title":"A multi-scale directional line detector for retinal vessel segmentation","volume":"19","author":"Khawaja","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110304_b0130","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10916-010-9466-3","article-title":"Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram","volume":"35","author":"Sun","year":"2011","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.bspc.2026.110304_b0135","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"10.1016\/j.bspc.2026.110304_b0140","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TMI.2015.2409024","article-title":"Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images","volume":"34","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110304_b0145","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neucom.2016.07.077","article-title":"Saliency driven vasculature segmentation with infinite perimeter active contour model","volume":"259","author":"Zhao","year":"2017","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.bspc.2026.110304_b0150","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1080\/09687630801889440","article-title":"Comparison of active contour models for image segmentation in X-ray coronary angiogram images","volume":"32","author":"Nirmala Devi","year":"2008","journal-title":"J. Med. Eng. Technol."},{"key":"10.1016\/j.bspc.2026.110304_b0155","doi-asserted-by":"crossref","unstructured":"M. Taghizadeh, S. Sadri, A.M. Doosthoseini, Segmentation of coronary vessels by combining detection of centerlines and active contour model, in: Proc. Iranian Conf. Mach. Vis. Image Process., 2011, pp. 1\u20134.","DOI":"10.1109\/IranianMVIP.2011.6121536"},{"key":"10.1016\/j.bspc.2026.110304_b0160","doi-asserted-by":"crossref","unstructured":"J. Wang, S. Zhao, Z. Liu, Y. Tian, F. Duan, Y. Pan, An active contour model based on adaptive threshold for extraction of cerebral vascular structures, Comput. Math. Methods Med. (2016) Article ID.","DOI":"10.1155\/2016\/6472397"},{"key":"10.1016\/j.bspc.2026.110304_b0165","doi-asserted-by":"crossref","unstructured":"J. Brieva, E. Gonzalez, F. Gonzalez, A. Bousse, J.J. Bellanger, A level set method for vessel segmentation in coronary angiography, in: Proc. IEEE EMBC, 2006, pp. 6348\u20136351.","DOI":"10.1109\/IEMBS.2005.1615949"},{"issue":"1","key":"10.1016\/j.bspc.2026.110304_b0170","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TBME.2007.896587","article-title":"Vessel extraction under non-uniform illumination: a level set approach","volume":"55","author":"Sum","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110304_b0175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-13-169","article-title":"3D vasculature segmentation using localized hybrid level-set method","volume":"13","author":"Hong","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.bspc.2026.110304_b0180","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.compbiomed.2015.09.008","article-title":"Segmentation of retinal vessels by means of directional response vector similarity and region growing","volume":"66","author":"L\u00e1z\u00e1r","year":"2015","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.bspc.2026.110304_b0185","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/TBME.2015.2403295","article-title":"Iterative vessel segmentation of fundus images","volume":"62","author":"Roychowdhury","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110304_b0190","doi-asserted-by":"crossref","unstructured":"D.S.D. Lara, A.W.C. Faria, A.A. Ara\u00fajo, D. Menotti, A semi-automatic method for segmentation of the coronary artery tree from angiography, in: Proc. Brazilian Symp. Comput. Graph. Image Process., 2009, pp. 194\u2013201.","DOI":"10.1109\/SIBGRAPI.2009.41"},{"issue":"1","key":"10.1016\/j.bspc.2026.110304_b0195","first-page":"1","article-title":"Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking","volume":"9","author":"Zhou","year":"2010","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.bspc.2026.110304_b0200","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.cmpb.2018.01.002","article-title":"Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging","volume":"157","author":"Wan","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110304_b0205","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-26947-5","article-title":"Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN","author":"Mondal","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110304_b0210","doi-asserted-by":"crossref","unstructured":"E. Nasr-Esfahani, S. Samavi, N. Karimi, S.M.R. Soroushmehr, K. Ward, M.H. Jafari, B. Felfeliyan, B. Nallamothu, K. Najarian, Vessel extraction in X-ray angiograms using deep learning, in: Proc. IEEE EMBC, 2016, pp. 643\u2013646.","DOI":"10.1109\/EMBC.2016.7590784"},{"key":"10.1016\/j.bspc.2026.110304_b0215","doi-asserted-by":"crossref","unstructured":"R. Phellan, A. Peixinho, A. Falc\u00e3o, N.D. Forkert, Vascular segmentation in TOF MRA images of the brain using a deep convolutional neural network, in: Proc. MICCAI Workshops, 2017, pp. 39\u201346.","DOI":"10.1007\/978-3-319-67534-3_5"},{"key":"10.1016\/j.bspc.2026.110304_b0220","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.bspc.2026.110304_b0225","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","article-title":"Retinal blood vessel segmentation using fully convolutional network with transfer learning","volume":"68","author":"Jiang","year":"2018","journal-title":"Comput. Med. Imag. Graph."},{"key":"10.1016\/j.bspc.2026.110304_b0230","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.cmpb.2019.06.030","article-title":"Scale-space approximated convolutional neural networks for retinal vessel segmentation","volume":"178","author":"Noh","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110304_b0235","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3389\/fnins.2019.00097","article-title":"A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease","volume":"13","author":"Livne","year":"2019","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.bspc.2026.110304_b0240","series-title":"Proc. ECCV","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.bspc.2026.110304_b0245","unstructured":"HuBMAP Consortium, Kaggle dataset and code repository, 2023."},{"key":"10.1016\/j.bspc.2026.110304_b0250","unstructured":"F. Isensee, J. Petersen, S.A.A. Kohl, P.F. J\u00e4ger, K.H. Maier-Hein, nnU-Net: breaking the spell on successful medical image segmentation, arXiv (2019)."},{"key":"10.1016\/j.bspc.2026.110304_b0255","series-title":"Proc. MICCAI","first-page":"520","article-title":"A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations","author":"Roth","year":"2014"},{"issue":"23","key":"10.1016\/j.bspc.2026.110304_b0260","doi-asserted-by":"crossref","first-page":"9070","DOI":"10.3390\/ijms21239070","article-title":"A computational framework based on ensemble deep neural networks for essential genes identification","volume":"21","author":"Le","year":"2020","journal-title":"Int. J. Mol. Sci."},{"issue":"2","key":"10.1016\/j.bspc.2026.110304_b0265","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1007\/s42044-025-00243-x","article-title":"ADNet: deep learning based model for Alzheimer\u2019s disease classification","volume":"8","author":"Upadhyay","year":"2025","journal-title":"Iran J. Comput. Sci."},{"key":"10.1016\/j.bspc.2026.110304_b0270","article-title":"Multimodal fusion and cutting-edge AI-based smart vending machines for electronic component management","author":"Karthiga","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.110304_b0275","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-024-80448-5","article-title":"EEG-based smart emotion recognition using meta-heuristic optimization and hybrid deep learning techniques","volume":"14","author":"Karthiga","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110304_b0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107729","article-title":"Optimized Alzheimer disorder classification with DACN-MFFN utilizing OBLDE-TDO enhanced deep neural network features","volume":"106","author":"Karthiga","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110304_b0285","unstructured":"S. Koyamada, Y. Shikauchi, K. Nakae, M. Koyama, S. Ishii, Deep learning of fMRI big data: a novel approach to subject-transfer decoding, arXiv (2015)."},{"key":"10.1016\/j.bspc.2026.110304_b0290","unstructured":"S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, arXiv (2015)."},{"issue":"2","key":"10.1016\/j.bspc.2026.110304_b0295","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":"Nat. Methods"},{"key":"10.1016\/j.bspc.2026.110304_b0300","article-title":"PathBot: a foundation model for pathological image analysis","author":"Lu","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110304_b0305","article-title":"Harnessing text insights with visual alignment for medical image segmentation","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Med Imag."},{"key":"10.1016\/j.bspc.2026.110304_b0310","article-title":"Segment together: a versatile paradigm for semi-supervised medical image segmentation","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Med. Imag."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942600858X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S174680942600858X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:45:00Z","timestamp":1777592700000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S174680942600858X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":62,"alternative-id":["S174680942600858X"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110304","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated microvascular segmentation in histology images using WSDetect-Net: a hybrid deep learning approach","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110304","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110304"}}