LAUSR

In nonclinical toxicity studies, stage-aware evaluation is often expected to assess drug-induced testicular toxicity. Although stage-aware evaluation does not require identification of specific stages, it is important to understand microscopic features of spermatogenic staging. Staging of the spermatogenic cycle in dogs is a challenging and time-consuming process. In this study, we first defined morphologic features for the eight spermatogenic stages in standard histology sections (H&E slides) of dog testes. For image analysis, we defined the key morphologic features of five stages/pooled stage groups (I-II, III-IV, V, VI-VII, and VIII). These criteria were used to develop a deep learning (DL) algorithm for staging of the spermatogenic cycle of control dog testes using whole slide images. In addition, a DL-based nucleus segmentation model was trained to detect and quantify the number of different germ cells, including spermatogonia, spermatocytes, and spermatids. Identification of spermatogenic stages and quantification of germ cell populations were successfully automated by the DL models. Combining these two algorithms provided color-coding visual spermatogenic staging and quantitative information on germ cell populations at specific stages that would facilitate the stage-aware evaluation and detection of changes in germ cell populations in nonclinical toxicity studies.