Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation

• URL: http://arxiv.org/abs/2503.21829v1
• Date: Wed, 26 Mar 2025 14:07:52 GMT
• Title: Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation
• Authors: Ivan Diaz, Florin Scherer, Yanik Berli, Roland Wiest, Helly Hammer, Robert Hoepner, Alejandro Leon Betancourt, Piotr Radojewski, Richard McKinley,
• Abstract summary: In MRI, differences in between vendors, hospitals and sequences can yield highly inhomogeneous imaging data.<n>For clinical applications, algorithms must be trained to handle data with various voxel resolutions.<n>We present a network architecture designed to be able to learn directly from spatially heterogeneous data, without resampling.
• Score: 32.93762295714261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the setting of clinical imaging, differences in between vendors, hospitals and sequences can yield highly inhomogeneous imaging data. In MRI in particular, voxel dimension, slice spacing and acquisition plane can vary substantially. For clinical applications, therefore, algorithms must be trained to handle data with various voxel resolutions. The usual strategy to deal with heterogeneity of resolution is harmonization: resampling imaging data to a common (usually isovoxel) resolution. This can lead to loss of fidelity arising from interpolation artifacts out-of-plane and downsampling in-plane. We present in this paper a network architecture designed to be able to learn directly from spatially heterogeneous data, without resampling: a segmentation network based on the e3nn framework that leverages a spherical harmonic, rather than voxel-grid, parameterization of convolutional kernels, with a fixed physical radius. Networks based on these kernels can be resampled to their input voxel dimensions. We trained and tested our network on a publicly available dataset assembled from three centres, and on an in-house dataset of Multiple Sclerosis cases with a high degree of spatial inhomogeneity. We compared our approach to a standard U-Net with two strategies for handling inhomogeneous data: training directly on the data without resampling, and resampling to a common resolution of 1mm isovoxels. We show that our network is able to learn from various combinations of voxel sizes and outperforms classical U-Nets on 2D testing cases and most 3D testing cases. This shows an ability to generalize well when tested on image resolutions not seen during training. Our code can be found at: http://github.com/SCAN-NRAD/e3nn\_U-Net.

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