Atlas-based Anatomical Modeling and Analysis of Heart Disease

By Pau Medrano-Gracia, B. Cowan, Avan Suinesiaputra, A. Young. 2014

11 citations recorded by the source corpus.

Heart shape and function are major determinants of disease severity and predictors of future morbidity and mortality. Many studies now rely on non-invasive cardiac imaging techniques to quantify structural and functional changes. Statistical anatomical modeling of heart shape and motion provides a new tool for the quantification and evaluation of heart disease. This review surveys recent progress in the evaluation of statistical shape measures across populations and sub-cohorts, and highlights collaborative efforts to facilitate data sharing and atlas-based shape analysis.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Geometry and Topology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Daniel Rueckert
2. Wenjia Bai
3. Martin Rajchl
4. Ozan Oktay
5. Giacomo Tarroni

In Computer Vision and Pattern Recognition

1. Wenjia Bai
2. Daniel Rueckert
3. S. Cook
4. D. O’Regan
5. Martin Rajchl
6. Ozan Oktay
7. Giacomo Tarroni
8. Xiahai Zhuang

In Geometry and Topology

1. Daniel Rueckert

Citations in the corpus

• Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018, methodology): “Given the high dimensionality of medical images, a popular approach in the literature is to analyze them by constructing statistical shape models of the heart using finite elements models or segmentation and co-registration algorithms to derive subject-specific meshes [8,9].”