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

arXiv:2404.17967v1 Announce Type: new
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 …

abstract arxiv computational cs.cv data demand diagnostics framework images imaging medical medical research modeling planning prediction research resources ssm statistical statistics treatment type