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Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation

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Biomedical Image Registration (WBIR 2022)

Abstract

Hybrid methods that combine learning-based features with conventional optimisation have become popular for medical image registration. The ConvexAdam algorithm that ranked first in the comprehensive Learn2Reg registration challenges completely decouples semantic and/or hand-crafted feature extraction from the estimation of the transformation due to the difficulty of differentiating the discrete optimisation step. In this work, we propose a simple extension that enables backpropagation through discrete optimisation and learns to fuse the semantic and hand-crafted features in a supervised setting. We demonstrate state-of-the-art performance on abdominal CT registration.

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Notes

  1. 1.

    https://github.com/MIRACLE-Center/CTSpine1K.

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Correspondence to Hanna Siebert .

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Siebert, H., Heinrich, M.P. (2022). Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_13

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  • DOI: https://doi.org/10.1007/978-3-031-11203-4_13

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  • Online ISBN: 978-3-031-11203-4

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