Deep <scp>SED‐Net</scp> with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules-Reference-Cited by-同舟云学术

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Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules

Published:2022-04-14 Issue:8 Volume:101 Page:658-674
ISSN:1552-4922
Container-title:Cytometry Part A
language:en
Short-container-title:Cytometry Pt A

Author:

Liang Shi¹,Lu Haoda¹,Zang Min²,Wang Xiangxue¹,Jiao Yiping¹,Zhao Tingting²,Xu Eugene Yujun²³,Xu Jun¹^ORCID

Affiliation:

1. Institute for AI in Medicine, School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

2. State Key Laboratory of Reproductive Medicine Nanjing Medical University Nanjing China

3. Department of Neurology, Center for Reproductive Sciences Northwestern University Feinberg School of Medicine Illinois USA

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.a.24556

Reference41 articles.

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

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

3. DAZL is a master translational regulator of murine spermatogenesis

4. A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal

5. Sertoli Cell Number Defines and Predicts Germ and Leydig Cell Population Sizes in the Adult Mouse Testis

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1. A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation;Computers in Biology and Medicine;2023-05

2. Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning;Journal of Assisted Reproduction and Genetics;2023-03-30

3. SATINN: an automated neural network-based classification of testicular sections allows for high-throughput histopathology of mouse mutants;Bioinformatics;2022-10-10

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