SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images

TL;DR

SCorP is a novel framework that predicts surface correspondences directly from unsegmented medical images, using an unsupervised shape prior to improve accuracy and efficiency over traditional and deep learning methods.

Contribution

It introduces an unsupervised shape prior to directly predict dense correspondences from unsegmented images, removing the need for supervised shape models and reducing linearity limitations.

Findings

Eliminates the need for supervised shape models.

Improves correspondence prediction accuracy.

Streamlines training and inference processes.

Abstract

Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional methods for shape modeling from imaging data demand significant manual and computational resources. Additionally, these methods necessitate repeating the entire modeling pipeline to derive shape descriptors (e.g., surface-based point correspondences) for new data. While deep learning approaches have shown promise in streamlining the construction of SSMs on new data, they still rely on traditional techniques to supervise the training of the deep networks. Moreover, the predominant linearity assumption of traditional approaches restricts their efficacy, a limitation also inherited by deep learning models trained using optimized/established correspondences. Consequently, representing complex anatomies becomes challenging. To address these limitations, we introduce SCorP, a novel framework capable of predicting surface-based correspondences directly from unsegmented images. By leveraging the shape prior learned directly from surface meshes in an unsupervised manner, the proposed model eliminates the need for an optimized shape model for training supervision. The strong shape prior acts as a teacher and regularizes the feature learning of the student network to guide it in learning image-based features that are predictive of surface correspondences. The proposed model streamlines the training and inference phases by removing the supervision for the correspondence prediction task while alleviating the linearity assumption.