Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis

Spermatogenesis in mammals is a cyclic process of spermatogenic cell development in the seminiferous epithelium that can be subdivided into 12 subsequent stages. Histological staging analysis of testis sections, specifically of seminiferous tubule cross-sections, is the only effective method to evaluate the quality of the spermatogenic process and to determine developmental defects leading to infertility. Such staging analysis, however, is tedious and time-consuming, and it may take a long time to become proficient. We now have developed a Computerized Staging system of Spermatogenesis (CSS) for mouse testis sections through learning of an expert with decades of experience in mouse testis staging. The development of the CSS system comprised three major parts: 1) Developing computational image analysis models for mouse testis sections; 2) Automated classification of each seminiferous tubule cross-section into three stage groups: Early Stages (ES: stages I-V), Middle Stages (MS: stages VI-VIII), and Late Stages (LS: stages IV-XII); 3) Automated classification of MS into distinct stages VI, VII-mVIII, and late VIII based on newly developed histomorphological features. A cohort of 40 H&E stained normal mouse testis sections was built according to three modules where 28 cross-sections were leveraged for developing tubule region segmentation, spermatogenic cells types and multi-concentric-layers segmentation models. The rest of 12 testis cross-sections, approximately 2314 tubules whose stages were manually annotated by two expert testis histologists, served as the basis for developing the CSS system. The CSS system's accuracy of mean and standard deviation (MSD) in identifying ES, MS, and LS were 0.93 ± 0.03, 0.94 ± 0.11, and 0.89 ± 0.05 and 0.85 ± 0.12, 0.88 ± 0.07, and 0.96 ± 0.04 for one with 5 years of experience, respectively. The CSS system's accuracy of MSD in identifying stages VI, VII-mVIII, and late VIII are 0.74 ± 0.03, 0.85 ± 0.04, and 0.78 ± 0.06 and 0.34 ± 0.18, 0.78 ± 0.16, and 0.44 ± 0.25 for one with 5 years of experience, respectively. In terms of time it takes to collect these data, it takes on average 3 hours for a histologist and 1.87 hours for the CSS system to finish evaluating an entire testis section (computed with a PC (I7-6800k 4.0 GHzwith 32GB of RAM & 256G SSD) and a Titan 1080Ti GPU). Therefore, the CSS system is more accurate and faster compared to a human histologist in staging, and further optimization and development will not only lead to a complete staging of all 12 stages of mouse spermatogenesis but also could aid in the future diagnosis of human infertility. Moreover, the top-ranking histomorphological features identified by the CSS classifier are consistent with the primary features used by histologists in discriminating stages VI, VII-mVIII, and late VIII.

[1]  L. Russell,et al.  Histological and Histopathological Evaluation of the Testis , 1990 .

[2]  E. Xu,et al.  A novel requirement in mammalian spermatid differentiation for the DAZ-family protein Boule. , 2010, Human molecular genetics.

[3]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[4]  A. Madabhushi,et al.  Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.

[5]  Anant Madabhushi,et al.  Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings , 2019, Breast Cancer Research.

[6]  C. L. Luengo Hendriks,et al.  New computerized staging method to analyze mink testicular tissue in environmental research , 2017, Environmental toxicology and chemistry.

[7]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Shaoting Zhang,et al.  Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images , 2019, Journal of medical imaging.

[9]  A. Madabhushi,et al.  Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer , 2018, BMC Cancer.

[10]  Mrinal K. Mandal,et al.  Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters , 2017, IEEE Journal of Biomedical and Health Informatics.

[11]  Edward J. Delp,et al.  Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction , 2018, Computational Imaging.

[12]  A. Madabhushi,et al.  Computer extracted features of cancer nuclei from H&E stained tissues of tumor predicts response to nivolumab in non-small cell lung cancer. , 2018 .

[13]  David J. Hawkes,et al.  Automated Classification of Breast Cancer Stroma Maturity From Histological Images , 2017, IEEE Transactions on Biomedical Engineering.

[14]  Mohammad Sohel Rahman,et al.  MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation , 2019, Neural Networks.

[15]  E. Oakberg A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal. , 1956, The American journal of anatomy.

[16]  Cris L. Luengo Hendriks,et al.  Analyzing Tubular Tissue in Histopathological Thin Sections , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[17]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  George Lee,et al.  Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings. , 2016, European urology focus.

[19]  Anant Madabhushi,et al.  Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule , 2019, ECDP.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Hao Chen,et al.  Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..

[22]  Mengyi Zhu,et al.  DAZL is a master translational regulator of murine spermatogenesis , 2018, bioRxiv.

[23]  E. Ahmed,et al.  Staging of mouse seminiferous tubule cross-sections. , 2009, Methods in molecular biology.

[24]  Andrew Janowczyk,et al.  Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images , 2017, Scientific Reports.

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  R. Hess,et al.  Computer tracking of germ cells in the cycle of the seminiferous epithelium and prediction of changes in cycle duration in animals commonly used in reproductive biology and toxicology. , 1992, Journal of andrology.

[28]  Lewis D. Griffin,et al.  Using Basic Image Features for Texture Classification , 2010, International Journal of Computer Vision.

[29]  R. Hess,et al.  Spermatogenesis and cycle of the seminiferous epithelium. , 2008, Advances in experimental medicine and biology.

[30]  M. Meistrich,et al.  Assessment of spermatogenesis through staging of seminiferous tubules. , 2013, Methods in molecular biology.

[31]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[32]  A. Madabhushi,et al.  Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers , 2018, Laboratory Investigation.

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  Y. Clermont Kinetics of spermatogenesis in mammals: seminiferous epithelium cycle and spermatogonial renewal. , 1972, Physiological reviews.

[35]  Anant Madabhushi,et al.  A Quantitative Histomorphometric Classifier (QuHbIC) Identifies Aggressive Versus Indolent p16-Positive Oropharyngeal Squamous Cell Carcinoma , 2014, The American journal of surgical pathology.

[36]  Lewis D. Griffin The Second Order Local-Image-Structure Solid , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Cris L. Luengo Hendriks,et al.  Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut , 2013, ICIAP.