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The development of mouse spermatozoa is a continuous process from spermatogonia, spermatocytes, spermatids to mature sperm. Those developing germ cells (spermatogonia, spermatocyte, and spermatids) together with supporting sertoli cells are all enclosed inside seminiferous tubules of the testis, their identification is key to testis histology and pathology analysis. Automated segmentation of all these cells is a challenging task because of their dynamical changes in different stages. The accurate segmentation of testicular cells is critical in developing computerized spermatogenesis staging. In this paper, we present a novel segmentation model, SED‐Net, which incorporates a squeeze‐and‐excitation (SE) module and a dense unit. The SE module optimizes and obtains features from different channels, whereas the dense unit uses fewer parameters to enhance the use of features. A human‐in‐the‐loop strategy, named deep interactive learning, is developed to achieve better segmentation performance while reducing the workload of manual annotation and time consumption. Across a cohort of 274 seminiferous tubules from stages VI to VIII, the SED‐Net achieved a pixel accuracy of 0.930, a mean pixel accuracy of 0.866, a mean intersection over union of 0.710, and a frequency weighted intersection over union of 0.878, respectively, in terms of four types of testicular cell segmentation. There is no significant difference between manual annotated tubules and segmentation results by SED‐Net in cell composition analysis for tubules from stages VI to VIII. In addition, we performed cell composition analysis on 2346 segmented seminiferous tubule images from 12 segmented testicular section results. The results provided quantitation of cells of various testicular cell types across 12 stages. The rule reflects the cell variation tendency across 12 stages during development of mouse spermatozoa. The method could enable us to not only analyze cell morphology and staging during the development of mouse spermatozoa but also potentially could be applied to the study of reproductive diseases such as infertility.

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Motivation Differentiating 12 stages of mouse seminiferous epithelial cycle is vital towards understanding the dynamic spermatogenesis process. However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I-III from Stages IV-V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice. To achieve this, we propose a novel pipeline for Computerized Spermatogenesis Staging (CSS). Results The CSS pipeline comprises four parts: 1) A seminiferous tubule segmentation model is developed to extract every single tubule; 2) A Multi-Scale Learning (MSL) model is developed to integrate local and global information of a seminiferous tubule to distinguish Stages I-V from Stages VI-XII; 3) A Multi-Task Learning (MTL) model is developed to segment the Multiple Testicular Cells (MTCs) for Stages I-V without an exhaustive requirement for manual annotation; 4) A set of 204-dimensional image-derived features is developed to discriminate Stages I-III from Stages IV-V by capturing cell-level and image-level representation. Experimental results suggest that the proposed MSL and MTL models outperform classic single-scale and single-task models when manual annotation is limited. In addition, the proposed image-derived features are discriminative between Stages I-III and Stages IV-V. In conclusion, the CSS pipeline can not only provide histologists with a solution to facilitate quantitative analysis for spermatogenesis stage identification but also help them to uncover novel computerized image-derived biomarkers. Availability and implementation https://github.com/jydada/CSS Supplementary information Supplementary data are available at Bioinformatics online.

RNA作为一类非常重要的生物分子, 以messenger RNA(mRNA), transfer RNA(tRNA), small nucleolar RNA(snoRNA), long non-coding RNA(lncRNA), Piwi-interacting RNA(piRNA), microRNA(miRNA)和circular RNA(circRNA)等多种形态参与到生命活动的各个方 面 [1~3] . RNA结合蛋白(RNA -binding proteins, RBPs)是