Abstract
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists’ work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained nuclei classification into two cross-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited to the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Delahunt, B., Eble, J.N., Egevad, L., Samaratunga, H.: Grading of renal cell carcinoma. Histopathology 74(1), 4–17 (2019). https://doi.org/10.1111/his.13735
Delahunt, B., ET AL.: The international society of urological pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am. J. Surgical Pathol. 37(10), 1490–1504 (2013)
Zhao, R., et al.: Rethinking dice loss for medical image segmentation. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 851–860 (2020). https://doi.org/10.1109/ICDM50108.2020.00094
Xie, C., et al.: Recist-net: Lesion detection via grouping keypoints on recist-based annotation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 921–924 (2021). https://doi.org/10.1109/ISBI48211.2021.9433794
Zhang, X., et al.: Classifying breast cancer histopathological images using a robust artificial neural network architecture. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds.) IWBBIO 2019. LNCS, vol. 11465, pp. 204–215. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17938-0_19
Naylor, P., Laé, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448–459 (2019). https://doi.org/10.1109/TMI.2018.2865709
Li, J., Hu, Z., Yang, S.: Accurate nuclear segmentation with center vector encoding. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 394–404. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_30
Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019). https://doi.org/10.1016/j.media.2019.101563
Qu, H., et al.: Joint segmentation and fine-grained classification of nuclei in histopathology images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 900–904 (2019). https://doi.org/10.1109/ISBI.2019.8759457
Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2020). https://doi.org/10.1109/TPAMI.2020.2983686
Liu, Y., et al.: Cbnet: a novel composite backbone network architecture for object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11653–1166, April 2020. https://doi.org/10.1609/aaai.v34i07.6834
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, October 2019
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML 2009, p. 41–48. Association for Computing Machinery, New York (2009). https://doi.org/10.1145/1553374.1553380
Kang, Q., Lao, Q., Fevens, T.: Nuclei segmentation in histopathological images using two-stage learning. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, pp. 703–711. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_78
Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process. 122, 1–13 (2016). https://doi.org/10.1016/j.sigpro.2015.11.011
Gamper, J., et al.: Pannuke dataset extension, insights and baselines. arXiv preprint arXiv:2003.10778 (2020)
Verma, R., Kumar, N., Patil, A., Kurian, N., Rane, S., Sethi, A.: Multi-organ nuclei segmentation and classification challenge 2020 (2020). https://doi.org/10.13140/RG.2.2.12290.02244/1
Puttapirat, P., et al.: Openhi - an open source framework for annotating histopathological image. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1076–1082 (2018). https://doi.org/10.1109/BIBM.2018.8621393
Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: Panoptic segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9396–9405 (2019). https://doi.org/10.1109/CVPR.2019.00963
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2017
Raza, S.E.A., et al.: Micro-net: a unified model for segmentation of various objects in microscopy images. Med. Image Anal. 52, 160–173 (2019). https://doi.org/10.1016/j.media.2018.12.003
Gao, Z., Puttapirat, P., Shi, J., Li, C.: Renal cell carcinoma detection and subtyping with minimal point-based annotation in whole-slide images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 439–448. Springer (2020). https://doi.org/10.1007/978-3-030-59722-1_42
Acknowledgements
This work has been supported by the National Key Research and Development Program of China (2018YFC0910404); This work has been supported by National Natural Science Foundation of China (61772409); The consulting research project of the Chinese Academy of Engineering (The Online and Offline Mixed Educational Service System for “The Belt and Road” Training in MOOC China); Project of China Knowledge Centre for Engineering Science and Technology; The innovation team from the Ministry of Education (IRT_17R86); and the Innovative Research Group of the National Natural Science Foundation of China (61721002). The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, Z. et al. (2021). Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-87237-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87236-6
Online ISBN: 978-3-030-87237-3
eBook Packages: Computer ScienceComputer Science (R0)
