{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:28:52Z","timestamp":1773116932606,"version":"3.50.1"},"reference-count":15,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"DOI":"10.13039\/100000135","name":"NIH Blueprint for Neuroscience Research","doi-asserted-by":"publisher","award":["R01DE030508"],"award-info":[{"award-number":["R01DE030508"]}],"id":[{"id":"10.13039\/100000135","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005161","name":"Ethel and James Flinn Foundation","doi-asserted-by":"publisher","award":["21K04"],"award-info":[{"award-number":["21K04"]}],"id":[{"id":"10.13039\/100005161","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods have demonstrated to outperform previous intensity- or pattern-based methods. However, the heavy computation of deep learning provides impression of lagging response in real time and hampered the adoptability of these models in routine research. We developed and implemented NuKit a deep learning platform, which accelerates nucleus segmentation and provides prompt results to the users. NuKit platform consists of two deep learning models coupled with an interactive graphical user interface (GUI) to provide fast and automatic nucleus segmentation \u201con the fly\u201d. Both deep learning models provide complementary tasks in nucleus segmentation. The whole image segmentation model performs whole image nucleus whereas the click segmentation model supplements the nucleus segmentation with user-driven input to edits the segmented nuclei. We trained the NuKit whole image segmentation model on a large public training data set and tested its performance in seven independent public image data sets. The whole image segmentation model achieves average [Formula: see text] and [Formula: see text]. The outputs could be exported into different file formats, as well as provides seamless integration with other image analysis tools such as QuPath. NuKit can be executed on Windows, Mac, and Linux using personal computers. <\/jats:p>","DOI":"10.1142\/s0219720023500026","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T07:48:02Z","timestamp":1673509682000},"source":"Crossref","is-referenced-by-count":5,"title":["NuKit: A deep learning platform for fast nucleus segmentation of histopathological images"],"prefix":"10.1142","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2199-7332","authenticated-orcid":false,"given":"Ching-Nung","family":"Lin","sequence":"first","affiliation":[{"name":"Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA"}]},{"given":"Christine H.","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Head and Neck Endocrine Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2955-8369","authenticated-orcid":false,"given":"Aik Choon","family":"Tan","sequence":"additional","affiliation":[{"name":"Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA"}]}],"member":"219","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"S0219720023500026BIB001","doi-asserted-by":"crossref","first-page":"754641","DOI":"10.3389\/frai.2021.754641","volume":"4","author":"Lee K","year":"2021","journal-title":"Front Artif Intell"},{"issue":"10","key":"S0219720023500026BIB002","doi-asserted-by":"crossref","first-page":"R100","DOI":"10.1186\/gb-2006-7-10-r100","volume":"7","author":"Carpenter AE","year":"2006","journal-title":"Genome Biol"},{"issue":"1","key":"S0219720023500026BIB003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"Bankhead P","year":"2017","journal-title":"Sci Rep"},{"key":"S0219720023500026BIB004","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.tcb.2021.12.004","volume":"32","author":"Hollandi R","year":"2022","journal-title":"Trends Cell Biol"},{"key":"S0219720023500026BIB005","doi-asserted-by":"crossref","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"Graham S","year":"2019","journal-title":"Med Image Anal"},{"key":"S0219720023500026BIB007","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/978-3-319-24574-4_33","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"Kainz P","year":"2015"},{"key":"S0219720023500026BIB008","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fbioe.2019.00053","volume":"7","author":"Vu QD","year":"2019","journal-title":"Front Bioeng Biotechnol"},{"issue":"7","key":"S0219720023500026BIB009","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"Kumar N","year":"2017","journal-title":"IEEE Trans. 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