Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule

Jun Xu, Haoda Lu, Haixin Li, Xiangxue Wang, Anant Madabhushi, Yujun Xu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Whole slide image (WSI) of mouse testicular cross-section contains hundreds of seminiferous tubules. Meanwhile, each seminiferous tubule also contains different types of germ cells among different histological regions. These factors make it a challenge to segment distinct germ cells and regions on mouse testicular cross-section. Automated segmentation of different germ cells and regions is the first step to develop a computerized spermatogenesis staging system. In this paper, a set of 28 H&E stained WSIs of mouse testicular cross-section and 209 Stage VI-VIII tubules images were studied to develop an automated multi-task segmentation model. A deep residual network (ResNet) is first presented for seminiferous tubule segmentation from mouse testicular cross-section. According to the types and distribution of germ cells in the tubules, we then present the other deep ResNet for multi-cell (spermatid, spermatocyte, and spermatogonia) segmentation and a fully convolutional network (FCN) for multi-region (elongated spermatid, round spermatid, and spermatogonial & spermatocyte regions) segmentation. To our knowledge, this is the first time to develop a computerized model for analyzing histopathological image of mouse testis. Three segmentation models presented in this paper show good segmentation performance and obtain the pixel accuracy of 94.40%, 91.26%, 93.47% for three segmentation tasks, respectively, which lays a solid foundation for the establishment of mouse spermatogenesis staging system.

Original languageEnglish (US)
Title of host publicationDigital Pathology - 15th European Congress, ECDP 2019, Proceedings
EditorsConstantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030239367
StatePublished - 2019
Event15th European Congress on Digital Pathology, ECDP 2019 - Warwick, United Kingdom
Duration: Apr 10 2019Apr 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Congress on Digital Pathology, ECDP 2019
Country/TerritoryUnited Kingdom


  • Deep learning
  • Germ cell segmentation
  • Mouse testis histology
  • Seminiferous tubules
  • Whole slide image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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