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Volumetric image segmentation with convolutional neural networks (CNNs)encounters several challenges, which are specific to medical images. Amongthese challenges are large volumes of interest, high class imbalances, anddifficulties in learning shape representations. To tackle these challenges, wepropose to improve over traditional CNN-based volumetric image segmentationthrough point-wise classification of point clouds. The sparsity of point cloudsallows processing of entire image volumes, balancing highly imbalancedsegmentation problems, and explicitly learning an anatomical shape. We buildupon PointCNN, a neural network proposed to process point clouds, and proposehere to jointly encode shape and volumetric information within the point cloudin a compact and computationally effective manner. We demonstrate how thisapproach can then be used to refine CNN-based segmentation, which yieldssignificantly improved results in our experiments on the difficult task ofperipheral nerve segmentation from magnetic resonance neurography images. Bysynthetic experiments, we further show the capability of our approach inlearning an explicit anatomical shape representation.

Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation