{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T10:02:49Z","timestamp":1779962569382,"version":"3.53.1"},"reference-count":23,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pathology International"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Deep learning enhances quantitative analysis of blood vessels in whole\u2010slide images (WSIs), leading to precise and reliable counting. However, counting vessels in lymph nodes\u2014which contain distinctive high endothelial venules (HEVs)\u2014has not been systematically evaluated. Artificial intelligence (AI) approaches to vessel\u2010counting fall into two categories: semantic segmentation, which provides a continuous mask, and instance segmentation, which treats each vessel as a separate object; prior work has emphasized the former. To address this gap, we adopted an instance\u2010level approach to detect and count individual vessels. We developed a convolutional neural network (CNN) to quantify CD34\u2010positive vessels in lymph\u2010node WSIs, using U\u2010Net, a widely used biomedical segmentation architecture. The model was trained over 4.0 million (4.0\u2009M) iterations, followed by a fixed post\u2010processing procedure to extract vessel instances. At 3.0\u2009M, the model most faithfully captured CD34\u2010positive vascular morphology. In the validation set, this checkpoint showed the highest morphological similarity. Furthermore, detection performance was robust with minimal variation across lymph nodes and training checkpoints. Using a simple fixed procedure, our method achieves accurate vessel detection and counting in lymph\u2010node WSIs. This model provides a practical basis for standardized quantification and for studying lymph\u2010node vascular morphology across normal, inflammatory, and lymphomatous states.<\/jats:p>","DOI":"10.1111\/pin.70125","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T05:43:33Z","timestamp":1778564613000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Standardized Instance\u2010Level Quantification of CD34\u2010Positive Vessels in Lymph Node Whole\u2010Slide Images Using U\u2010Net"],"prefix":"10.1111","volume":"76","author":[{"given":"Satomi","family":"Omotani","sequence":"first","affiliation":[{"name":"Department of Pathology, Faculty of Medical Sciences University of Fukui Eiheiji Fukui Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natsumi","family":"Yonemoto","sequence":"additional","affiliation":[{"name":"Department of Pathology, Faculty of Medical Sciences University of Fukui Eiheiji Fukui Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akifumi","family":"Muramoto","sequence":"additional","affiliation":[{"name":"Department of Pathology, Faculty of Medical Sciences University of Fukui Eiheiji Fukui Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7607-0801","authenticated-orcid":false,"given":"Motohiro","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Department of Pathology, Faculty of Medical Sciences University of Fukui Eiheiji Fukui Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/path.2851"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/pin.12294"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1369\/00221554211048551"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10456-021-09792-8"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0000000000000948"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2024.12.033"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0508-1"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_2_13_11_1","first-page":"234","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention \u2013 MICCAI 2015 (Lecture Notes in Computer Science; vol. 9351)","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2055-z"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41374-019-0272-3"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom13091327"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbinf.2023.1296667"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15153773"},{"key":"e_1_2_13_19_1","first-page":"850","article-title":"Efficient Non\u2010Maximum Suppression","volume":"3","author":"Neubeck A.","year":"2006","journal-title":"Proceedings of Internationals Conference Pattern Recognition"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800020109"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.2307\/1932409"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8137.1912.tb05611.x"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(86)90837-8"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcm.2016.02.012"}],"container-title":["Pathology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/pin.70125","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/pin.70125","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/pin.70125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:13:23Z","timestamp":1779959603000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/pin.70125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":23,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1111\/pin.70125"],"URL":"https:\/\/doi.org\/10.1111\/pin.70125","archive":["Portico"],"relation":{},"ISSN":["1320-5463","1440-1827"],"issn-type":[{"value":"1320-5463","type":"print"},{"value":"1440-1827","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"2025-11-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70125"}}