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However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I\u2013III from Stages IV\u2013V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice. To achieve this, we propose a novel pipeline for computerized spermatogenesis staging (CSS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The CSS pipeline comprises four parts: (i) A seminiferous tubule segmentation model is developed to extract every single tubule; (ii) A multi-scale learning (MSL) model is developed to integrate local and global information of a seminiferous tubule to distinguish Stages I\u2013V from Stages VI\u2013XII; (iii) a multi-task learning (MTL) model is developed to segment the multiple testicular cells for Stages I\u2013V without an exhaustive requirement for manual annotation; (iv) A set of 204D image-derived features is developed to discriminate Stages I\u2013III from Stages IV\u2013V by capturing cell-level and image-level representation. Experimental results suggest that the proposed MSL and MTL models outperform classic single-scale and single-task models when manual annotation is limited. In addition, the proposed image-derived features are discriminative between Stages I\u2013III and Stages IV\u2013V. In conclusion, the CSS pipeline can not only provide histologists with a solution to facilitate quantitative analysis for spermatogenesis stage identification but also help them to uncover novel computerized image-derived biomarkers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>https:\/\/github.com\/jydada\/CSS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac677","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T17:41:30Z","timestamp":1666287690000},"page":"5307-5314","source":"Crossref","is-referenced-by-count":4,"title":["A novel pipeline for computerized mouse spermatogenesis staging"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4434-7869","authenticated-orcid":false,"given":"Haoda","family":"Lu","sequence":"first","affiliation":[{"name":"Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044, China"},{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"},{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University , Singapore 636921, Singapore"}]},{"given":"Min","family":"Zang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Reproductive Medicine, Nanjing Medical University , Nanjing 211166, China"}]},{"given":"Gabriel Pik Liang","family":"Marini","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"}]},{"given":"Xiangxue","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044, China"}]},{"given":"Yiping","family":"Jiao","sequence":"additional","affiliation":[{"name":"Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044, China"}]},{"given":"Nianfei","family":"Ao","sequence":"additional","affiliation":[{"name":"Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044, China"}]},{"given":"Kokhaur","family":"Ong","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"}]},{"given":"Xinmi","family":"Huo","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"}]},{"given":"Longjie","family":"Li","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"}]},{"given":"Eugene Yujun","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Reproductive Medicine, Nanjing Medical University , Nanjing 211166, China"},{"name":"Department of Neurology, Center for Reproductive Sciences, Northwestern University Feinberg School of Medicine , IL 60611, USA"},{"name":"Cellular Screening Center, The University of Chicago , IL 60637, USA"}]},{"given":"Wilson Wen Bin","family":"Goh","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University , Singapore 636921, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5092-1009","authenticated-orcid":false,"given":"Weimiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR , Singapore 138673, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5315-8811","authenticated-orcid":false,"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044, China"}]}],"member":"286","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"2022113016200935900_btac677-B1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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