Self-Supervised Discovery of Anatomical Shape Landmarks.

ABSTRACT: Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discov

SUBMITTER: Bhalodia R 

PROVIDER: S-EPMC7993653 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature