# Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation

## TL;DR

This paper introduces a method that uses point cloud processing to improve volumetric medical image segmentation by explicitly learning shape representations, leading to better accuracy and shape understanding.

## Contribution

It proposes a novel approach combining point cloud encoding with CNNs to enhance shape learning and segmentation in medical images.

## Key findings

- Significantly improved segmentation accuracy on nerve images
- Effective learning of explicit anatomical shape representations
- Enhanced processing of large, imbalanced volumetric data

## 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.