Deep learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy

Abstract

Infertility is a global issue, with approximately 50% of cases attributed to defective spermatogenesis. For studies into spermatogenesis and spermatogenic dysfunction, evaluating the seminiferous tubule stage is essential. However, the current method of evaluation involves labor-intensive and time-consuming manual tasks such as staining, observation, and image analysis. Lack of reproducibility is also a problem owing to the subjective nature of visual evaluation by experts. In this study, we propose a deep learning-based method for automatically and objectively evaluating the seminiferous tubule stage. Our approach automatically predicts which of 12 seminiferous tubule stages is represented in bright-field microscopic images of mouse seminiferous tubules stained by hematoxylin-PAS. For training and validation of our model, we created a dataset of 1229 tissue images, each labeled with one of 12 distinct seminiferous tubule stages. The maximum prediction accuracy was 79.58% which rose to 98.33% with allowance for a prediction error of {+/-}1 stage. Remarkably, although the model was not explicitly trained on the patterns of transition between stages, it inferred characteristic structural patterns involved in the process of spermatogenesis. This method not only advances our understanding of spermatogenesis but also holds promise for improving the automated diagnosis of infertility.