Deep Learning-Based Spermatogenic Staging Assessment for Hematoxylin and Eosin-Stained Sections of Rat Testes.

In preclinical toxicology studies, a "stage-aware" histopathological evaluation of testes is recognized as the most sensitive method to detect effects on spermatogenesis. A stage-aware evaluation requires the pathologist to be able to identify the different stages of the spermatogenic cycle. Classically, this evaluation has been performed using periodic acid-Schiff (PAS)-stained sections to visualize the morphology of the developing spermatid acrosome, but due to the complexity of the rat spermatogenic cycle and the subtlety of the criteria used to distinguish between the 14 stages of the cycle, staging of tubules is not only time consuming but also requires specialized training and practice to become competent. Using different criteria, based largely on the shape and movement of the elongating spermatids within the tubule and pooling some of the stages, it is possible to stage tubules using routine hematoxylin and eosin (H&E)-stained sections, thereby negating the need for a special PAS stain. These criteria have been used to develop an automated method to identify the stages of the rat spermatogenic cycle in digital images of H&E-stained Wistar rat testes. The algorithm identifies the spermatogenic stage of each tubule, thereby allowing the pathologist to quickly evaluate the testis in a stage-aware manner and rapidly calculate the stage frequencies.