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

Abstract

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
Pages117-124
Number of pages8
ISBN (Print)9783030239367
DOIs
StatePublished - Jan 1 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

Conference

Conference15th European Congress on Digital Pathology, ECDP 2019
CountryUnited Kingdom
CityWarwick
Period4/10/194/13/19

Keywords

  • 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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  • Cite this

    Xu, J., Lu, H., Li, H., Wang, X., Madabhushi, A., & Xu, Y. (2019). Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule. In C. C. Reyes-Aldasoro, A. Janowczyk, M. Veta, P. Bankhead, & K. Sirinukunwattana (Eds.), Digital Pathology - 15th European Congress, ECDP 2019, Proceedings (pp. 117-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11435 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-23937-4_14