S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences

• URL: http://arxiv.org/abs/2304.07515v2
• Date: Mon, 24 Jul 2023 08:10:52 GMT
• Title: S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
• Authors: Lennart Bastian, Alexander Baumann, Emily Hoppe, Vincent B\"urgin, Ha Young Kim, Mahdi Saleh, Benjamin Busam, Nassir Navab
• Abstract summary: We propose an unsupervised method to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods. Our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations.
• Score: 91.48841778012782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications. However, they typically require domain expertise, and labor-intensive landmark annotations to construct. We address these shortcomings by proposing an unsupervised method that leverages deep geometric features and functional correspondences to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods, even on highly irregular surface topologies. We demonstrate this for two different anatomical structures: the thyroid and a multi-chamber heart dataset. Furthermore, our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations without manual segmentation annotation.

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