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Abstract #2438

Rotation-Equivariant Deep Learning for Diffusion MRI

Philip Müller1, Vladimir Golkov1, Valentina Tomassini2, and Daniel Cremers1
1Computer Vision Group, Technical University of Munich, Munich, Germany, 2D’Annunzio University, Chieti–Pescara, Italy

Convolutional networks are successful, but have recently been outperformed by new neural networks that are equivariant under rotations and translations. These new networks do not struggle with learning each possible orientation of each image feature separately. So far, they have been proposed for 2D and 3D data. Here we generalize them to 6D diffusion MRI data, ensuring joint equivariance under 3D roto-translations in image space and the matching rotations in q-space, as dictated by the image formation. We validate our method on multiple-sclerosis lesion segmentation. Our proposed neural networks yield better results and require less training data.

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