Abstract
Deep learning based methods have become the most popular approach for prostate segmentation in MRI. However, domain variations due to the complex acquisition process result in textural differences as well as imaging artefacts which significantly affects the robustness of deep learning models for prostate segmentation across multiple sites. We tackle this problem by using multiple MRI sequences to learn a set of low dimensional shape components whose combinatorially large learnt composition is capable of accounting for the entire distribution of segmentation outputs. We draw on the language of cellular sheaf theory to model compositionality driven by local and global topological correctness. In our experiments, our method significantly improves the domain generalisability of anatomical and tumour segmentation of the prostate. Code is available at https://github.com/AinkaranSanthi/A-Sheaf-Theoretic-Perspective-for-Robust-Segmentation.git.
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Acknowledgements
This work was supported and funded by Cancer Research UK (CRUK) (C309/A28804).
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Santhirasekaram, A., Pinto, K., Winkler, M., Rockall, A., Glocker, B. (2023). A Sheaf Theoretic Perspective for Robust Prostate Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_24
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