Abstract
The morphology statistics of colon glands is a key feature for pathologists to diagnose colorectal cancer. Current gland instance segmentation methods show good overall performances, but accurate segmentation of extremely deformed glands in highly malignant cases or some rare benign cases remains to be challenging . In this paper, we propose a hybrid model that learns hierarchical semantic feature matching from histological pairs in an attentive process, where both spatial details and morphological appearances can be well preserved and balanced, especially for the glands with severe deformation. A consistency loss function is also introduced to enforce simultaneous satisfaction of semantic correspondence and gland instance segmentation on the pixel-level. The novel proposed model is validated on two publicly available colon gland datasets GlaS and CRAG. The model successfully boosts the segmentation performances on greatly mutated or deformed cases, and outperforms the state-of-the-art approaches.
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
Aljanabi, M.A., Hussain, Z.M., Lu, S.F.: An entropy-histogram approach forimage similarity and face recognition. Mathematical Problems in Engineering 2018, (2018)
Awan, R., Sirinukunwattana, K., Epstein, D., Jefferyes, S., Qidwai, U., Aftab, Z., Mujeeb, I., Snead, D., Rajpoot, N.: Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci. Reports 7(1), 16852 (2017)
Bosman, F.T., Carneiro, F., Hruban, R.H., Theise, N.D., et al.: WHO classification of tumours of the digestive system. No. 4 En., World Health Organization, (2010)
Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2487–2496 (2016)
Fleming, M., Ravula, S., Tatishchev, S.F., Wang, H.L.: Colorectal carcinoma: pathologic aspects. J. Gastrointestinal Oncol. 3(3), 153 (2012)
Graham, S., et al.: Mild-net: minimal information loss dilated network for gland instance segmentation in colon histology images. Med. Image Anal. 52, 199–211 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., Kautz, J.: Improving landmark localization with semi-supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1546–1555 (2018)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)
Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 66–75 (2017)
Lee, J., Kim, D., Ponce, J., Ham, B.: Sfnet: learning object-aware semantic correspondence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2278–2287 (2019)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)
Qu, H., Yan, Z., Riedlinger, G.M., De, S., Metaxas, D.N.: Improving nuclei/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 378–386. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_42
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489–502 (2017)
Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA: A Cancer J. Clin. 65(2), 87–108 (2015)
Xie, Y., Lu, H., Zhang, J., Shen, C., Xia, Y.: Deep segmentation-emendation model for gland instance segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 469–477. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_52
Xu, X., et al.: Quantization of fully convolutional networks for accurate biomedical image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8300–8308. IEEE (2018)
Xu, Y., Li, Y., Liu, M., Wang, Y., Lai, M., Eric, I., Chang, C.: Gland instance segmentation by deep multichannel side supervision. In: Ourselin, S., Joskowicz, L., Sabuncu, M., 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
Xu, Y., Li, Y., Wang, Y., Liu, M., Fan, Y., Lai, M., Eric, I., Chang, C.: Gland instance segmentation using deep multichannel neural networks. IEEE Trans. Biomed. Eng. 64(12), 2901–2912 (2017)
Yan, Z., Yang, X., Cheng, K.T.T.: A deep model with shape-preserving loss for gland instance segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 138–146. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_16
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_46
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint, (2015) arXiv:1511.07122
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_47
Zheng, H., et al.: Biomedical image segmentation via representative annotation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5901–5908 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, P., Chung, A.C.S. (2020). Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_61
Download citation
DOI: https://doi.org/10.1007/978-3-030-59861-7_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59860-0
Online ISBN: 978-3-030-59861-7
eBook Packages: Computer ScienceComputer Science (R0)