PointCHD: A Point Cloud Benchmark for Congenital Heart Disease Classification and Segmentation

Abstract

Congenital heart disease (CHD) is one of the most common birth defects. Due to the lack of data and the difficulty of labeling, CHD datasets are scarce. Previous studies focused on CT and other medical image modalities, while point cloud is still unexplored. Point cloud can intuitively model organ shapes, which has obvious advantages in medical analysis and diagnosis assistance. However, the production of medical point cloud dataset is more complex than that of image dataset, and the 3D modeling of internal organs needs to be reconstructed after scanning by high-precision instruments. We propose PointCHD, the first point cloud dataset for CHD diagnosis, with a large number of high precision-annotated and wide-categorized data. PointCHD includes different types of three-dimensional data with varying degrees of distortion, and supports multiple analysis tasks, i.e., classification, segmentation, reconstruction, etc. We also construct a benchmark on PointCHD with the goal of medical diagnosis, we design the analysis process and compare the performances of mainstream point cloud analysis methods. In view of the complex internal and external structures of heart point cloud, we propose a point cloud representation method based on manifold learning. By introducing normals to consider the surface continuity to construct a manifold learning method of adaptive projection plane, we can fully extract the structural features of heart, and achieve the best performance on each task of PointCHD benchmark. Finally, we summarize the existing problems of CHD point cloud analysis and prospects for potential future research directions.