A novel pipeline for computerized mouse spermatogenesis staging.

Area of Science:

• Reproductive Biology
• Computational Pathology
• Bioinformatics

Background:

• Accurate staging of the 12 mouse seminiferous epithelial cycle stages is crucial for understanding spermatogenesis.
• Morphological similarities between adjacent stages pose significant challenges for manual identification.
• Distinguishing specific stages (I-III vs. IV-V) is vital for analyzing normal sperm development and infertile conditions.

Purpose of the Study:

• To develop a novel computerized spermatogenesis staging (CSS) pipeline.
• To facilitate accurate and efficient identification of spermatogenesis stages in mouse testes.
• To aid histologists in quantitative analysis and biomarker discovery.

Main Methods:

• Developed a seminiferous tubule segmentation model.
• Implemented a multi-scale learning (MSL) model for distinguishing broad stage groups (I-V vs. VI-XII).
• Utilized a multi-task learning (MTL) model for segmenting testicular cells in early stages (I-V).
• Engineered 204D image-derived features for fine-grained discrimination between Stages I-III and IV-V.

Main Results:

• The proposed MSL and MTL models demonstrated superior performance compared to traditional models, especially with limited manual annotation.
• The developed image-derived features effectively discriminated between Stages I-III and Stages IV-V.
• The CSS pipeline successfully integrates multiple computational approaches for robust staging.

Conclusions:

• The CSS pipeline offers a valuable tool for quantitative analysis of spermatogenesis staging.
• This approach can assist researchers in identifying spermatogenic defects and uncovering novel image-derived biomarkers.
• The pipeline enhances the ability to study dynamic processes in male reproductive biology.