Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds

• URL: http://arxiv.org/abs/2405.09707v1
• Date: Wed, 15 May 2024 21:13:54 GMT
• Title: Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds
• Authors: Jadie Adams, Shireen Elhabian,
• Abstract summary: Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. Point2SSM++ is a principled, self-supervised deep learning approach that learns correspondence points from point cloud representations of anatomical shapes. We present principled extensions of Point2SSM++ tailored for dynamictemporal and multi-anatomy scenarios.
• Score: 4.972323953932128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterization and quantification of anatomical shapes such as bones and organs, aiding in pathology and disease diagnostics and treatment planning. Despite its potential, SSM remains under-utilized in medical research due to the significant overhead associated with automatic construction methods, which demand complete, aligned shape surface representations. Additionally, optimization-based techniques rely on bias-inducing assumptions or templates and have prolonged inference times as the entire cohort is simultaneously optimized. To overcome these challenges, we introduce Point2SSM++, a principled, self-supervised deep learning approach that directly learns correspondence points from point cloud representations of anatomical shapes. Point2SSM++ is robust to misaligned and inconsistent input, providing SSM that accurately samples individual shape surfaces while effectively capturing population-level statistics. Additionally, we present principled extensions of Point2SSM++ to adapt it for dynamic spatiotemporal and multi-anatomy use cases, demonstrating the broad versatility of the Point2SSM++ framework. Furthermore, we present extensions of Point2SSM++ tailored for dynamic spatiotemporal and multi-anatomy scenarios, showcasing the broad versatility of the framework. Through extensive validation across diverse anatomies, evaluation metrics, and clinically relevant downstream tasks, we demonstrate Point2SSM++'s superiority over existing state-of-the-art deep learning models and traditional approaches. Point2SSM++ substantially enhances the feasibility of SSM generation and significantly broadens its array of potential clinical applications.

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