{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T23:34:50Z","timestamp":1790552090943,"version":"4.1.0"},"reference-count":51,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100010785","name":"Canada First Research Excellence Fund","doi-asserted-by":"crossref","award":["Medicine by Design"],"award-info":[{"award-number":["Medicine by Design"]}],"id":[{"id":"10.13039\/501100010785","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["Discovery Grant"],"award-info":[{"award-number":["Discovery Grant"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Tissue Engineering Part C: Methods"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>Semantic segmentation was performed on 177 large histological images of re-endothelialized mouse lung vasculatures. Specifically, patch-based semantic segmentation algorithms were used to classify pixels corresponding to two classes: organ tissue, which includes lung and heart tissue; and ruptured and\/or dilated vessels, which are abnormal vessels formed during re-endothelialization. Semantic segmentation is a potential means to automate the end-to-end analysis of these images, circumventing denoising and enhancement operations to visualize tissue and bypassing the manual diagnosis of ruptured and\/or dilated vessels. To increase data quantity, images were compressed to sizes 1024 \u00d7 1024, 768 \u00d7 768, and 512 \u00d7 512 and then divided into nonoverlapping 256 \u00d7 256 patches. To benchmark the performance of the patch-based models, a vanilla model trained on complete images compressed to size 256 \u00d7 256 was also evaluated. The U-Net and LinkNet architectures were used to train and test each model using a data augmentation and transfer learning approach, and their results were ensembled. The loss of image context in the 3 \u00d7 3 and 4 \u00d7 4 patch-based models negatively impacted performance, generating many false positive predictions for target classes, whereas the low-image quantity of the vanilla model hindered performance. The 2 \u00d7 2 ensemble patch-based model returned the best performance, classifying organ tissue with a precision, recall, and intersection over union (IOU) of 88.0% \u00b1 5.7%, 84.7% \u00b1 9.2%, and 76.3% \u00b1 10.9%, respectively, and classifying ruptured\/dilated vessels with a precision, recall, and IOU of 78.4% \u00b1 5.2%, 60.2% \u00b1 11.4%, and 51.0% \u00b1 8.4%, respectively.<\/jats:p>\n                  <jats:sec>\n                    <jats:title>Impact Statement<\/jats:title>\n                    <jats:p>To evaluate re-endothelization quality, the absence of ruptured and dilated vessels must be confirmed in the resultant re-endothelialized lung scaffold through subjective, work-intensive, and time-consuming per-image manual analysis. In this study, we apply computer vision to detect the ruptured and\/or dilated vessels from re-endothelialized histology images via end-to-end semantic segmentation. We also investigate the viability of a patch-based semantic segmentation approach to detect ruptured and\/or dilated vessels and generate high-resolution masks. Through this work, the potential of computer vision to automate and standardize the characterization of recellularized lung histology is demonstrated.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19373384261458003","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:03:15Z","timestamp":1780999395000},"page":"248-259","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Abnormal Vessel Detection in Re-Endothelialized Mouse Lungs: A Proof of Concept"],"prefix":"10.1177","volume":"32","author":[{"given":"Joshua","family":"Paciocco","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Taniguchi","sequence":"additional","affiliation":[{"name":"Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Kamata","sequence":"additional","affiliation":[{"name":"Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Amon","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."},{"name":"Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Golnaz","family":"Karoubi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."},{"name":"Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada."},{"name":"Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9594-4930","authenticated-orcid":false,"given":"Aimy","family":"Bazylak","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(21)00511-7"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.healun.2014.08.004"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.04.00065004"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.14503\/THIJ-18-6749"},{"key":"e_1_3_5_6_2","volume-title":"Lung Transplant Rejection","author":"Mrad A","year":"2021","unstructured":"Mrad A, , Chakraborty R. 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