<ul><li>Researchers have developed a network architecture for segmenting multiple sclerosis lesions from spatially inhomogeneous MRI data without resampling.</li><li>The network is based on the e3nn framework and leverages a spherical harmonic parameterization of convolutional kernels, allowing it to be resampled to input voxel dimensions.</li><li>The network outperformed a standard U-Net when tested on both 2D and most 3D cases of multiple sclerosis lesions.</li><li>The approach demonstrates the ability to learn from various combinations of voxel sizes and generalize well to testing cases with different image resolutions.</li></ul>

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

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