Point2SSM++: Self-supervised learning of anatomical shape models from point clouds.

Area of Science:

• Medical imaging analysis
• Computational anatomy
• Machine learning in healthcare

Background:

• Statistical shape modeling (SSM) is crucial for analyzing anatomical shapes in clinical research.
• Current SSM methods require extensive data preprocessing and have limitations like bias and long inference times.
• This under-utilization hinders advancements in pathology diagnostics and treatment planning.

Purpose of the Study:

• To introduce Point2SSM++, a self-supervised deep learning approach for automated SSM construction.
• To overcome limitations of existing SSM methods, including data alignment and computational overhead.
• To enhance the feasibility and clinical applicability of SSM.

Main Methods:

• Developed Point2SSM++, a deep learning framework that learns correspondence points directly from point cloud data.
• The method is self-supervised and robust to misaligned and inconsistent input data.
• Extended Point2SSM++ for dynamic spatiotemporal and multi-anatomy scenarios.

Main Results:

• Point2SSM++ accurately samples individual shape surfaces and captures population-level statistics.
• Demonstrated superiority over state-of-the-art deep learning models and traditional SSM approaches.
• Validated across diverse anatomies and clinically relevant tasks.

Conclusions:

• Point2SSM++ significantly enhances the feasibility of generating statistical shape models.
• The framework broadens the potential clinical applications of SSM in medical research.
• Point2SSM++ offers a robust and efficient solution for morphometric analysis.