Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis
Xu, Jun; Lu, Haoda; Li, Haixin; Yan, Chaoyang; Wang, Xiangxue; Zang, Min; de Rooij, Dirk G.; Madabhushi, Anant; Xu, Eugene Yujun
(2021) Medical Image Analysis, volume 70, pp. 1 - 15
(Article)
Abstract
Spermatogenesis in mammals is a cyclic process of spermatogenic cell development in the seminiferous epithelium that can be subdivided into 12 subsequent stages. Histological staging analysis of testis sections, specifically of seminiferous tubule cross-sections, is the only effective method to evaluate the quality of the spermatogenic process and to determine
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developmental defects leading to infertility. Such staging analysis, however, is tedious and time-consuming, and it may take a long time to become proficient. We now have developed a Computerized Staging system of Spermatogenesis (CSS) for mouse testis sections through learning of an expert with decades of experience in mouse testis staging. The development of the CSS system comprised three major parts: 1) Developing computational image analysis models for mouse testis sections; 2) Automated classification of each seminiferous tubule cross-section into three stage groups: Early Stages (ES: stages I-V), Middle Stages (MS: stages VI-VIII), and Late Stages (LS: stages IV-XII); 3) Automated classification of MS into distinct stages VI, VII-mVIII, and late VIII based on newly developed histomorphological features. A cohort of 40 H&E stained normal mouse testis sections was built according to three modules where 28 cross-sections were leveraged for developing tubule region segmentation, spermatogenic cells types and multi-concentric-layers segmentation models. The rest of 12 testis cross-sections, approximately 2314 tubules whose stages were manually annotated by two expert testis histologists, served as the basis for developing the CSS system. The CSS system's accuracy of mean and standard deviation (MSD) in identifying ES, MS, and LS were 0.93 ± 0.03, 0.94 ± 0.11, and 0.89 ± 0.05 and 0.85 ± 0.12, 0.88 ± 0.07, and 0.96 ± 0.04 for one with 5 years of experience, respectively. The CSS system's accuracy of MSD in identifying stages VI, VII-mVIII, and late VIII are 0.74 ± 0.03, 0.85 ± 0.04, and 0.78 ± 0.06 and 0.34 ± 0.18, 0.78 ± 0.16, and 0.44 ± 0.25 for one with 5 years of experience, respectively. In terms of time it takes to collect these data, it takes on average 3 hours for a histologist and 1.87 hours for the CSS system to finish evaluating an entire testis section (computed with a PC (I7-6800k 4.0 GHzwith 32GB of RAM & 256G SSD) and a Titan 1080Ti GPU). Therefore, the CSS system is more accurate and faster compared to a human histologist in staging, and further optimization and development will not only lead to a complete staging of all 12 stages of mouse spermatogenesis but also could aid in the future diagnosis of human infertility. Moreover, the top-ranking histomorphological features identified by the CSS classifier are consistent with the primary features used by histologists in discriminating stages VI, VII-mVIII, and late VIII.
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Keywords: Computerized staging of spermatogenesis, Deep learning, Mouse testicular section images, Mouse testis histology, Seminiferous tubules, Sperm development, Spermatogenic cell segmentation, Radiological and Ultrasound Technology, Radiology Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, Health Informatics, Computer Graphics and Computer-Aided Design
ISSN: 1361-8415
Publisher: Elsevier
Note: Funding Information: We would like to thank Dr. Rex Hess and members of Xu Lab for discussion and/or comments on this work. This work was supported by National Natural Science Foundation of China (Nos.U1809205, 61771249, 91959207, 81871352, 31771652, 81270737, and 81401256); National Basic Research Program of China (2015CB943002); Natural Science Foundation of Jiangsu Province of China (No.BK20181411); Special Foundation by Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) and Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT) (No. 2020xtzx005); Qing Lan Project of Jiangsu Province. Research reported in this publication was also supported by the National Cancer Institute of the National Institutes of Health under award numbers: 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01; National Institute for Biomedical Imaging and Bioengineering: 1R43EB028736-01; National Center for Research Resources under award number: 1C06 RR12463-01; VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service; The DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668; The DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558); The DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440); The DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329); The Ohio Third Frontier Technology Validation Fund; The Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and The Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government. Funding Information: We would like to thank Dr. Rex Hess and members of Xu Lab for discussion and/or comments on this work. This work was supported by National Natural Science Foundation of China (Nos. U1809205, 61771249, 91959207, 81871352, 31771652, 81270737, and 81401256 ); National Basic Research Program of China ( 2015CB943002 ); Natural Science Foundation of Jiangsu Province of China (No.BK20181411); Special Foundation by Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) and Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT) (No. 2020xtzx005); Qing Lan Project of Jiangsu Province. Research reported in this publication was also supported by the National Cancer Institute of the National Institutes of Health under award numbers: 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01; National Institute for Biomedical Imaging and Bioengineering: 1R43EB028736-01; National Center for Research Resources under award number: 1C06 RR12463-01; VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service; The DoD Breast Cancer Research Program Breakthrough Level 1 Award W81XWH-19-1-0668; The DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558); The DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440); The DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329); The Ohio Third Frontier Technology Validation Fund; The Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and The Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. Publisher Copyright: © 2020 Elsevier B.V.
(Peer reviewed)