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STAGETOOL, a Novel Automated Approach for Mouse Testis Histological Analysis
Oliver Meikar1,2, Daniel Majoral3, Olli Heikkinen2
1Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia.
Endocrinology
|December 3, 2022
Summary
We developed STAGETOOL, an AI tool that accurately classifies mouse testis stages and cell types from histological images. This automated method significantly speeds up the analysis of spermatogenesis, aiding reproductive biology research.
Area of Science:
- Male reproductive biology
- Histology
- Computational biology
Background:
- Spermatogenesis requires precise cellular organization within seminiferous tubules, classified into distinct stages.
- Manual identification of these stages from testis cross-sections is time-consuming and requires expertise.
- Automated analysis is needed to efficiently study spermatogenesis and its defects.
Purpose of the Study:
- To develop an automated method for analyzing mouse testis histology.
- To accurately classify seminiferous tubule stages and cell types using deep learning.
- To facilitate research on spermatogenesis, including knockout models and protein expression.
Main Methods:
- Developed STAGETOOL, a convolutional deep neural network (CNN).
- Trained STAGETOOL on DAPI-stained mouse testis cross-sections at ×400 magnification.
- Evaluated classification accuracy for tubule stages and cell types.
Main Results:
- STAGETOOL achieved 99.1% accuracy in classifying seminiferous tubule stages.
- F1 scores for 9 cell types ranged from 0.80 to 0.98.
- Demonstrated STAGETOOL's utility in analyzing knockout models and protein expression patterns.
Conclusions:
- STAGETOOL is the first automated method for mouse testis histology using fluorescent labeling for stage and cell-type recognition.
- The tool accurately and rapidly analyzes spermatogenesis, comparable to expert histologists but faster.
- Represents a significant advancement for male reproductive biology research.
