{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:34:05Z","timestamp":1774866845764,"version":"3.50.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>This study delves into the crucial domain of sperm segmentation, a pivotal component of male infertility diagnosis. It explores the efficacy of diverse architectural configurations coupled with various encoders, leveraging frames from the VISEM dataset for evaluation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The pursuit of automated sperm segmentation led to the examination of multiple deep learning architectures, each paired with distinct encoders. Extensive experimentation was conducted on the VISEM dataset to assess their performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our study evaluated various deep learning architectures with different encoders for sperm segmentation using the VISEM dataset. While each model configuration exhibited distinct strengths and weaknesses, UNet++ with ResNet34 emerged as a top-performing model, demonstrating exceptional accuracy in distinguishing sperm cells from non-sperm cells. However, challenges persist in accurately identifying closely adjacent sperm cells. These findings provide valuable insights for improving automated sperm segmentation in male infertility diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The study underscores the significance of selecting appropriate model combinations based on specific diagnostic requirements. It also highlights the challenges related to distinguishing closely adjacent sperm cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This research advances the field of automated sperm segmentation for male infertility diagnosis, showcasing the potential of deep learning techniques. Future work should aim to enhance accuracy in scenarios involving close proximity between sperm cells, ultimately improving clinical sperm analysis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2024.1376546","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T16:13:03Z","timestamp":1725898383000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A modified U-Net to detect real sperms in videos of human sperm cell"],"prefix":"10.3389","volume":"7","author":[{"given":"Hanan","family":"Saadat","sequence":"first","affiliation":[]},{"given":"Mohammad Mehdi","family":"Sepehri","sequence":"additional","affiliation":[]},{"given":"Mahdi-Reza","family":"Borna","sequence":"additional","affiliation":[]},{"given":"Behnam","family":"Maleki","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/VCIP.2017.8305148","article-title":"LinkNet: exploiting encoder representations for efficient semantic segmentation","volume-title":"2017 IEEE Visual Communications and Image Processing (VCIP)","author":"Chaurasia","year":"2017"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1109\/CVPRW.2018.00307","volume-title":"Estimation of sperm concentration and total motility from microscopic videos of human semen samples","author":"Dewan","year":"2018"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.3390\/genes14020451","article-title":"Study on sperm-cell detection using YOLOv5 architecture with Labaled dataset","volume":"14","author":"Dobrovolny","year":"2023","journal-title":"Genes"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2015.03.005","article-title":"Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors","volume":"120","author":"Garc\u00eda-Olalla","year":"2015","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.cmpb.2015.08.013","article-title":"An efficient method for automatic morphological abnormality detection from human sperm images","volume":"122","author":"Ghasemian","year":"2015","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref6","first-page":"70","article-title":"A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform","volume":"2","author":"Gonzalez-Castro","year":"2009","journal-title":"Int. J. Imaging Robot."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"23.1","DOI":"10.5244\/C.2.23","article-title":"A combined corner and edge detector","volume-title":"Procedings of the Alvey vision conference 1988","author":"Harris","year":"1988"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"16770","DOI":"10.1038\/s41598-019-53217-y","article-title":"Machine learning-based analysis of sperm videos and participant data for male fertility prediction","volume":"9","author":"Hicks","year":"2019","journal-title":"Sci. Rep."},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.106","volume-title":"Feature pyramid networks for object detection","author":"Lin","year":"2017"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1111\/and.12093","article-title":"Computer-aided sperm analysis: past, present and future","volume":"46","author":"Lu","year":"2014","journal-title":"Andrologia"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"104687","DOI":"10.1016\/j.compbiomed.2021.104687","article-title":"Impact of transfer learning for human sperm segmentation using deep learning","volume":"136","author":"Mar\u00edn","year":"2021","journal-title":"Comput. Biol. 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Ther."},{"key":"ref15","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015","author":"Ronneberger","year":"2015"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"106062","DOI":"10.1016\/j.knosys.2020.106062","article-title":"Evolution of image segmentation using deep convolutional neural network: a survey","author":"Sultana","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1071\/RD17520","article-title":"CASA in the medical laboratory: CASA in diagnostic andrology and assisted conception","volume":"30","author":"Tomlinson","year":"2018","journal-title":"Reprod. Fertil. Dev."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","article-title":"MANet: a two-stage deep learning method for classification of COVID-19 from chest X-ray images","volume":"443","author":"Xu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1612.01105","article-title":"Pyramid scene parsing network","author":"Zhao","year":"2017","journal-title":"rXiv.1612.01105"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-00889-5_1","article-title":"UNet++: a nested U-Net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1376546\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T16:13:24Z","timestamp":1725898404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1376546\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":20,"alternative-id":["10.3389\/frai.2024.1376546"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1376546","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]},"article-number":"1376546"}}