Abstract
Whole slide image (WSI) of mouse testicular cross-section contains hundreds of seminiferous tubules. Meanwhile, each seminiferous tubule also contains different types of germ cells among different histological regions. These factors make it a challenge to segment distinct germ cells and regions on mouse testicular cross-section. Automated segmentation of different germ cells and regions is the first step to develop a computerized spermatogenesis staging system. In this paper, a set of 28 H&E stained WSIs of mouse testicular cross-section and 209 Stage VI-VIII tubules images were studied to develop an automated multi-task segmentation model. A deep residual network (ResNet) is first presented for seminiferous tubule segmentation from mouse testicular cross-section. According to the types and distribution of germ cells in the tubules, we then present the other deep ResNet for multi-cell (spermatid, spermatocyte, and spermatogonia) segmentation and a fully convolutional network (FCN) for multi-region (elongated spermatid, round spermatid, and spermatogonial & spermatocyte regions) segmentation. To our knowledge, this is the first time to develop a computerized model for analyzing histopathological image of mouse testis. Three segmentation models presented in this paper show good segmentation performance and obtain the pixel accuracy of 94.40%, 91.26%, 93.47% for three segmentation tasks, respectively, which lays a solid foundation for the establishment of mouse spermatogenesis staging system.
J. Xu, H. Lu and H. Li—are the joint first authors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Russell, L.D., Ettlin, R.A., Hikim, A.P.S., Clegg, E.D.: Histological and histopathological evaluation of the testis. Int. J. Androl. 16(1), 83–83 (1993)
Clermont, Y.: Kinetics of spermatogenesis in mammals: seminiferous epithelium cycle and spermatogonial renewal. Physiol. Rev. 52(1), 198–236 (1972)
Oakberg, E.F.: Duration of spermatogenesis in the mouse and timing of stages of the cycle of the seminiferous epithelium. Am. J. Anat. 99(3), 507–516 (1956)
Gurcan, M.N., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)
He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19(1), 221–248 (2017)
Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. Med. Image Comput. Comput. Assist. Interv. 16(Pt 2), 411–418 (2013)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
Xu, H., Lu, C., Berendt, R., Jha, N., Mandal, M.: Automatic nuclei detection based on generalized laplacian of gaussian filters. IEEE J. Biomed. Health Inform. 21(3), 826–837 (2016)
Xu, J., et al.: Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. J. Med. Imaging 6, 017501 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, J., Lu, H., Li, H., Wang, X., Madabhushi, A., Xu, Y. (2019). Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-23937-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23936-7
Online ISBN: 978-3-030-23937-4
eBook Packages: Computer ScienceComputer Science (R0)