Abstract
Volumetric image segmentation with convolutional neural networks (CNNs) encounters several challenges, which are specific to medical images. Among these challenges are large volumes of interest, high class imbalances, and difficulties in learning shape representations. To tackle these challenges, we propose to improve over traditional CNN-based volumetric image segmentation through point-wise classification of point clouds. The sparsity of point clouds allows processing of entire image volumes, balancing highly imbalanced segmentation problems, and explicitly learning an anatomical shape. We build upon PointCNN, a neural network proposed to process point clouds, and propose here to jointly encode shape and volumetric information within the point cloud in a compact and computationally effective manner. We demonstrate how this approach can then be used to refine CNN-based segmentation, which yields significantly improved results in our experiments on the difficult task of peripheral nerve segmentation from magnetic resonance neurography images. By synthetic experiments, we further show the capability of our approach in learning an explicit anatomical shape representation.
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Notes
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We remark that other point cloud architectures might also be feasible for the task at hand. Experiments with PointNet and PointNet++ [8], two popular architectures and pioneers in deep learning-based point cloud processing, showed slightly worse performance and are omitted here for clarity.
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Acknowledgement
This research was supported by the Swiss National Science Foundation (SNSF). The authors thank the NVIDIA Corporation for their GPU donation and Alain Jungo for fruitful discussions.
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Balsiger, F., Soom, Y., Scheidegger, O., Reyes, M. (2019). Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_31
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DOI: https://doi.org/10.1007/978-3-030-32245-8_31
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