{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:25:38Z","timestamp":1792787138944,"version":"4.5.0"},"reference-count":16,"publisher":"SPIE-Intl Soc Optical Eng","issue":"03","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health","award":["R01 R01HL169944"],"award-info":[{"award-number":["R01 R01HL169944"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Cancer Institute, National Institutes of Health","award":["75N91019D00024"],"award-info":[{"award-number":["75N91019D00024"]}]},{"name":"United States Department of Veterans Affairs Clinical Sciences R&D Service","award":["IK2 CX001785"],"award-info":[{"award-number":["IK2 CX001785"]}],"id":[{"id":"https:\/\/ror.org\/05rsv9s98","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Center for Advancing Translational Sciences of the National Institutes of Health","award":["TL1TR002244"],"award-info":[{"award-number":["TL1TR002244"]}],"id":[{"id":"https:\/\/ror.org\/04pw6fb54","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>PurposeMpox is a viral illness with symptoms similar to smallpox. A key clinical metric to monitor disease progression is the number of skin lesions. Manually counting mpox skin lesions is labor-intensive and susceptible to human error.ApproachWe previously developed an mpox lesion counting method based on the UNet segmentation model using 66 photographs from 18 patients. We have compared four additional methods: the instance segmentation methods Mask R-CNN, YOLOv8, and E2EC, in addition to a UNet++ model. We designed a patient-level leave-one-out experiment, assessing their performance using F1 score and lesion count metrics. Finally, we tested whether an ensemble of the networks outperformed any single model.ResultsMask R-CNN model achieved an F1 score of 0.75, YOLOv8 a score of 0.75, E2EC a score of 0.70, UNet++ a score of 0.81, and baseline UNet a score of 0.79. Bland-Altman analysis of lesion count performance showed a limit of agreement (LoA) width of 62.2 for Mask R-CNN, 91.3 for YOLOv8, 94.2 for E2EC, and 62.1 for UNet++, with the baseline UNet model achieving 69.1. The ensemble showed an F1 score performance of 0.78 and LoA width of 67.4.ConclusionsInstance segmentation methods and UNet-based semantic segmentation methods performed equally well in lesion counting. Furthermore, the ensemble of the trained models showed no performance increase over the best-performing model UNet, likely because errors are frequently shared across models. Performance is likely limited by the availability of high-quality photographs for this complex problem, rather than the methodologies used.<\/jats:p>","DOI":"10.1117\/1.jmi.12.3.034506","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:36:48Z","timestamp":1750307808000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["Mpox lesion counting with semantic and instance segmentation methods"],"prefix":"10.1117","volume":"12","author":[{"given":"Bohan","family":"Jiang","sequence":"first","affiliation":[{"name":"Tennessee Valley Healthcare System"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1496-9691","authenticated-orcid":false,"given":"Andrew J.","family":"McNeil","sequence":"additional","affiliation":[{"name":"Tennessee Valley Healthcare System, Nashville"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-9903","authenticated-orcid":false,"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David  W.","family":"House","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ. Medical Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Placide","family":"Mbala-Kingebeni","sequence":"additional","affiliation":[{"name":"National Institute of Biomedical Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier Tshiani","family":"Mbaya","sequence":"additional","affiliation":[{"name":"National Institute of Biomedical Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyra","family":"Silaphet","sequence":"additional","affiliation":[{"name":"Tennessee Valley Healthcare System"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lori E.","family":"Dodd","sequence":"additional","affiliation":[{"name":"National Institute of Allergy and Infectious Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward W.","family":"Cowen","sequence":"additional","affiliation":[{"name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronique","family":"Nussenblatt","sequence":"additional","affiliation":[{"name":"National Institute of Allergy and Infectious Diseases"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler","family":"Bonnett","sequence":"additional","affiliation":[{"name":"Frederick National Lab. for Cancer Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziche","family":"Chen","sequence":"additional","affiliation":[{"name":"Tennessee Valley Healthcare System"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4000-5485","authenticated-orcid":false,"given":"Inga","family":"Saknite","sequence":"additional","affiliation":[{"name":"Vanderbilt University Medical Center, Department of Dermatology, Nashville, Tennessee, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit M.","family":"Dawant","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2850-4740","authenticated-orcid":false,"given":"Eric R.","family":"Tkaczyk","sequence":"additional","affiliation":[{"name":"Tennessee Valley Healthcare System"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","reference":[{"key":"r1","doi-asserted-by":"publisher","DOI":"10.1086\/498155"},{"key":"r2","doi-asserted-by":"crossref","DOI":"10.1101\/2022.05.26.22273379","article-title":"Clinical characterization of human monkeypox infections in the Democratic Republic of the Congo","author":"Pittman","year":"2022"},{"key":"r3","article-title":"Clinical aspects of monkeypox in DRC","author":"Muyembe-Tamfum","year":"2022"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"r5","first-page":"4278","article-title":"Inception-v4, Inception-ResNet and the impact of residual connections on learning","author":"Szegedy","year":"2016"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2022.08.044"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00440"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.33.3.031204"},{"key":"r12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.167"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(86)90837-8"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.18141"}],"container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-03\/034506\/Mpox-lesion-counting-with-semantic-and-instance-segmentation-methods\/10.1117\/1.JMI.12.3.034506.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T23:08:15Z","timestamp":1790204895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-03\/034506\/Mpox-lesion-counting-with-semantic-and-instance-segmentation-methods\/10.1117\/1.JMI.12.3.034506.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,19]]},"references-count":16,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2025,5,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.12.3.034506","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,19]]}}}