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.
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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].
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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
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