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

Area of Science:

• Medical imaging
• Computer-aided diagnosis
• 3D data analysis

Background:

• Congenital heart disease (CHD) is a common birth defect, yet datasets for its analysis are scarce, especially using point cloud data.
• Existing research primarily utilizes CT and other medical imaging, leaving the potential of point cloud data for CHD diagnosis unexplored.
• Point cloud data offers intuitive 3D organ modeling, beneficial for medical analysis, but its dataset creation is complex.

Purpose of the Study:

• To introduce PointCHD, the first comprehensive point cloud dataset specifically designed for congenital heart disease (CHD) diagnosis.
• To establish a benchmark for evaluating point cloud analysis methods on CHD data.
• To propose a novel point cloud representation method for enhanced structural feature extraction in CHD analysis.

Main Methods:

• Development of PointCHD, a large-scale, high-precision annotated point cloud dataset encompassing diverse CHD types and distortions.
• Construction of a CHD point cloud analysis benchmark, including classification, segmentation, and reconstruction tasks.
• Proposal of a manifold learning-based point cloud representation method incorporating surface continuity via normals for adaptive projection.

Main Results:

• PointCHD provides a rich resource for CHD research, supporting multiple 3D analysis tasks.
• The proposed manifold learning method achieved superior performance across all benchmark tasks, effectively capturing complex heart structures.
• Comparison of mainstream point cloud methods on the benchmark dataset highlighted performance variations and identified areas for improvement.

Conclusions:

• PointCHD represents a significant advancement in creating 3D datasets for CHD analysis, addressing data scarcity.
• The developed manifold learning approach demonstrates the potential of advanced point cloud techniques for accurate CHD diagnosis.
• Future research should focus on further refining CHD point cloud analysis methods and exploring new applications.