Literature DB >> 33676102

Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis.

Jun Xu1, Haoda Lu2, Haixin Li3, Chaoyang Yan2, Xiangxue Wang4, Min Zang3, Dirk G de Rooij5, Anant Madabhushi6, Eugene Yujun Xu7.   

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 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.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computerized staging of spermatogenesis; Deep learning; Mouse testicular section images; Mouse testis histology; Seminiferous tubules; Sperm development; Spermatogenic cell segmentation

Year:  2020        PMID: 33676102      PMCID: PMC8046964          DOI: 10.1016/j.media.2020.101835

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  25 in total

1.  A novel requirement in mammalian spermatid differentiation for the DAZ-family protein Boule.

Authors:  Michael J W VanGompel; Eugene Yujun Xu
Journal:  Hum Mol Genet       Date:  2010-03-24       Impact factor: 6.150

2.  The second order local-image-structure solid.

Authors:  Lewis D Griffin
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2007-08       Impact factor: 6.226

3.  Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters.

Authors:  Richard Berendt; Naresh Jha; Mrinal Mandal
Journal:  IEEE J Biomed Health Inform       Date:  2016-03-21       Impact factor: 5.772

4.  Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.

Authors:  George Lee; Robert W Veltri; Guangjing Zhu; Sahirzeeshan Ali; Jonathan I Epstein; Anant Madabhushi
Journal:  Eur Urol Focus       Date:  2016-06-16

5.  MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Authors:  Nabil Ibtehaz; M Sohel Rahman
Journal:  Neural Netw       Date:  2019-09-04

Review 6.  Kinetics of spermatogenesis in mammals: seminiferous epithelium cycle and spermatogonial renewal.

Authors:  Y Clermont
Journal:  Physiol Rev       Date:  1972-01       Impact factor: 37.312

7.  DAZL is a master translational regulator of murine spermatogenesis.

Authors:  Haixin Li; Zhuqing Liang; Jian Yang; Dan Wang; Hanben Wang; Mengyi Zhu; Baobao Geng; Eugene Yujun Xu
Journal:  Natl Sci Rev       Date:  2018-12-28       Impact factor: 17.275

8.  Automated Classification of Breast Cancer Stroma Maturity From Histological Images.

Authors:  Sara Reis; Patrycja Gazinska; John H Hipwell; Thomy Mertzanidou; Kalnisha Naidoo; Norman Williams; Sarah Pinder; David J Hawkes
Journal:  IEEE Trans Biomed Eng       Date:  2017-02-07       Impact factor: 4.538

9.  A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma.

Authors:  James S Lewis; Sahirzeeshan Ali; Jingqin Luo; Wade L Thorstad; Anant Madabhushi
Journal:  Am J Surg Pathol       Date:  2014-01       Impact factor: 6.394

Review 10.  Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

Authors:  Kaustav Bera; Kurt A Schalper; David L Rimm; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Nat Rev Clin Oncol       Date:  2019-08-09       Impact factor: 66.675

View more
  4 in total

1.  Cell type-specific inference of differential expression in spatial transcriptomics.

Authors:  Rafael A Irizarry; Fei Chen; Dylan M Cable; Evan Murray; Vignesh Shanmugam; Simon Zhang; Luli S Zou; Michael Diao; Haiqi Chen; Evan Z Macosko
Journal:  Nat Methods       Date:  2022-09-01       Impact factor: 47.990

2.  Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine.

Authors:  Réka Eszter Sziva; Júlia Ács; Anna-Mária Tőkés; Ágnes Korsós-Novák; György L Nádasy; Nándor Ács; Péter Gábor Horváth; Anett Szabó; Haoran Ke; Eszter Mária Horváth; Zsolt Kopa; Szabolcs Várbíró
Journal:  Life (Basel)       Date:  2022-01-27

Review 3.  Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.

Authors:  Shima Mehrvar; Lauren E Himmel; Pradeep Babburi; Andrew L Goldberg; Magali Guffroy; Kyathanahalli Janardhan; Amanda L Krempley; Bhupinder Bawa
Journal:  J Pathol Inform       Date:  2021-11-01

4.  Proliferation and Apoptosis of Cat (Felis catus) Male Germ Cells during Breeding and Non-Breeding Seasons.

Authors:  Luisa Valentini; Rosa Zupa; Chrysovalentinos Pousis; Rezart Cuko; Aldo Corriero
Journal:  Vet Sci       Date:  2022-08-20
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.