Abstract
We propose DeepCRC, a topology-aware deep learning-based approach for automated colorectum and colorectal cancer (CRC) segmentation in routine abdominal CT scans. Compared with MRI and CT Colonography, regular CT has a broader application but is more challenging. Standard segmentation algorithms often induce discontinued colon prediction, leading to inaccurate or completely failed CRC segmentation. To tackle this issue, we establish a new 1D colorectal coordinate system that encodes the position information along the colorectal elongated topology. In addition to the regular segmentation task, we propose an auxiliary regression task that directly predicts the colorectal coordinate for each voxel. This task integrates the global topological information into the network embedding and thus improves the continuity of the colorectum and the accuracy of the tumor segmentation. To enhance the model’s architectural ability of modeling global context, we add self-attention layers to the model backbone, and found it complementary to the proposed algorithm. We validate our approach on a cross-validation of 107 cases and outperform nnUNet by an absolute margin of 1.3% in colorectum segmentation and 8.3% in CRC segmentation. Notably, we achieve comparable tumor segmentation performance with the human inter-observer (DSC: 0.646 vs. 0.639), indicating that our method has similar reproducibility as a human observer.
L. Yao and Y. Xia—Equal contribution.
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
Medical Segmentation Decathlon, Challenge Leaderboard. https://decathlon-10.grand-challenge.org/evaluation/challenge/leaderboard/
Antonelli, M., et al.: The medical segmentation decathlon. arXiv preprint arXiv:2106.05735 (2021)
Argilés, G., et al.: Localised colon cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 31(10), 1291–1305 (2020)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. ICLR (2021)
Huang, Y.J., et al.: 3-D Roi-aware U-net for accurate and efficient colorectal tumor segmentation. IEEE Trans. Cybern. 51(11), 5397–5408 (2020)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Jian, J., et al.: Fully convolutional networks (FCNs)-based segmentation method for colorectal tumors on T2-weighted magnetic resonance images. Australas. Phys. Eng. Sci. Med. 41(2), 393–401 (2018). https://doi.org/10.1007/s13246-018-0636-9
Jiang, Y., et al.: ALA-Net: adaptive lesion-aware attention network for 3D colorectal tumor segmentation. IEEE Trans. Med. Imaging 40(12), 3627–3640 (2021)
Jin, D., Iyer, K.S., Chen, C., Hoffman, E.A., Saha, P.K.: A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recogn. Lett. 76, 32–40 (2016)
Liu, X., et al.: Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network. Med. Phys. 46(8), 3532–3542 (2019)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Ni, T., Xie, L., Zheng, H., Fishman, E.K., Yuille, A.L.: Elastic boundary projection for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2109–2118 (2019)
Pei, Y., Mu, L., Fu, Y., He, K., Li, H., Guo, S., Liu, X., Li, M., Zhang, H., Li, X.: Colorectal tumor segmentation of CT scans based on a convolutional neural network with an attention mechanism. IEEE Access 8, 64131–64138 (2020)
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
Soomro, M.H., et al.: Automated segmentation of colorectal tumor in 3D MRI using 3D multiscale densely connected convolutional neural network. J. Healthc. Eng. 2019 (2019)
Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)
Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. arXiv preprint arXiv:2111.14791 (2021)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Wang, Y., et al.: Deep distance transform for tubular structure segmentation in CT scans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3833–3842 (2020)
Wolf, A.M., et al.: Colorectal cancer screening for average-risk adults: 2018 guideline update from the American cancer society. CA Cancer J. Clin. 68(4), 250–281 (2018)
Yao, J., Cai, J., Yang, D., Xu, D., Huang, J.: Integrating 3D geometry of organ for improving medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 318–326. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_36
Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)
Zheng, S., et al.: MDCC-Net: multiscale double-channel convolution U-Net framework for colorectal tumor segmentation. Comput. Biol. Med. 130, 104183 (2021)
Acknowledgements
This study was supported by the National Key Research and Development Program of China [grant number 2021YFF1201003], the National Science Fund for Distinguished Young Scholars [grant number 81925023], the National Natural Scientific Foundation of China [grant number 82072090].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yao, L. et al. (2022). DeepCRC: Colorectum and Colorectal Cancer Segmentation in CT Scans via Deep Colorectal Coordinate Transform. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-16437-8_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16436-1
Online ISBN: 978-3-031-16437-8
eBook Packages: Computer ScienceComputer Science (R0)
