Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.

ABSTRACT: The study of physiology demonstrates that the form (shape) of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for quantitative analysis of forms of anatomies, aiding in characterizing and identifying differences within a population of subjects. Despite its utility, the conventional SSM construction pipeline is often complex and time-consuming. Additionally, reliance on linearity assumptions further limits the model from capturing clinically relevant variations. Recent advancements in deep learning solutions enable the direct inference of SSM from unsegmented medical images, streamlining the process and improving accessibility. However, the new methods of SSM

SUBMITTER: Iyer K 

PROVIDER: S-EPMC11568407 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature