Abstract
Microvascular invasion (MVI) is a histological feature of hepatocellular carcinoma (HCC). It is the strongest feature related to HCC recurrence and survival after liver resection. However, its diagnosis is time consuming which requires pathologist to examine histopathological images at high resolution. A computer aided MVI detection system that improves the diagnosis efficiency and consistency is in demand. There are two challenges in MVI detection (1) MVI is formed by the same type of tumor cells as common cancer tissue (CCT) and (2) both MVI and CCT’s size varies significantly – from a few cells to thousands of cells. Inspired by pathologists’ routine reading procedure, we propose a 3-stage solution composed by cascaded networks to tackle this problem. In this framework, images are first analyzed by pixel-level cancer tissue segmentation, followed by region-level instance feature extraction, and then by slide-level comparison to detect MVI. To reduce inter-stage error accumulation, the system is designed in the way that later stage can learn and correct errors in the previous stages. To effectively conduct slide-level analysis, a novel convolutional graph neural network with short cut (sc-GCN) is proposed to solve the over-smoothing problem in classic GCN methods. Testing results on 90 WSI samples show that the proposed system achieves state-of-the-art performance on MVI detection.
H. Chen, K. Wang, Y. Zhu—co-first author; S. Cheng, J. Yao—co-corresponding author
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
References
Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA. Cancer J. Clin. 65, 87–108 (2015)
Rodríguez-Perálvarez, M., Luong, T.V., Andreana, L., Meyer, T., Dhillon, A.P., Burroughs, A.K.: A systematic review of microvascular invasion in hepatocellular carcinoma: diagnostic and prognostic variability. Ann. Surg. Oncol. 20, 325–339 (2013)
Deng, S., et al.: Deep learning in digital pathology image analysis: a survey. Front. Med. 14(4), 470–487 (2020). https://doi.org/10.1007/s11684-020-0782-9
Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)
Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L.: Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 893–901. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_99
Zhou, Y., Onder, O.F., Dou, Q., Tsougenis, E., Chen, H., Heng, P.-A.: CIA-Net: robust nuclei instance segmentation with contour-aware information aggregation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 682–693. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_53
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., Heng, P.-A.: DCAN: deep contour-aware networks for object instance segmentation from histology images. Med. Image Anal. 36, 135–146 (2017)
Mukhopadhyay, S., et al.: Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). Am. J. Surg. Pathol. 42, 39–52 (2018)
Schmitz, R., Madesta, F., Nielsen, M., Krause, J., Steurer, S., Werner, R., Rösch, T.: Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture. Med. Image Anal. 70, 101996 (2021)
Xu, Y., Li, Y., Liu, M., Wang, Y., Lai, M., Chang, E.-C.: Gland instance segmentation by deep multichannel side supervision. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 496–504. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_57
Li, W., Nguyen, V.-D., Liao, H., Wilder, M., Cheng, K., Luo, J.: Patch transformer for multi-tagging whole slide histopathology images. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 532–540. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_59
Raju, A., Yao, J., Haq, M.M., Jonnagaddala, J., Huang, J.: Graph attention multi-instance learning for accurate colorectal cancer staging. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 529–539. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_51
Li, R., Yao, J., Zhu, X., Li, Y., Huang, J.: Graph CNN for survival analysis on whole slide pathological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 174–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_20
Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide histopathological images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7234–7242 (2017)
Yao, J., Zhu, X., Huang, J.: Deep multi-instance learning for survival prediction from whole slide images. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 496–504. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_55
Saillard, C., et al.: Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 72, 2000–2013 (2020)
Cong, W.M., et al.: Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update. World J. Gastroenterol. 22, 9279 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132–7141 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (ICLR) (2017)
Jungo, A., Reyes, M.: Assessing reliability and challenges of uncertainty estimations for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_6
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
Chen, H. et al. (2021). From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks. 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_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-87237-3_19
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)
