Synthetic Data Generation for 3D Myocardium Deformation Analysis

TL;DR

This paper presents a novel synthetic data generation method with ground truth annotations to enhance 3D myocardium deformation analysis from CT scans, addressing data scarcity issues in cardiovascular imaging.

Contribution

It introduces a new synthetic data generation approach with detailed annotations, enabling improved training of myocardium deformation models from limited real datasets.

Findings

Synthetic data improves model training accuracy.

Method facilitates development of reliable deformation analysis algorithms.

Code availability promotes reproducibility and further research.

Abstract

Accurate analysis of 3D myocardium deformation using high-resolution computerized tomography (CT) datasets with ground truth (GT) annotations is crucial for advancing cardiovascular imaging research. However, the scarcity of such datasets poses a significant challenge for developing robust myocardium deformation analysis models. To address this, we propose a novel approach to synthetic data generation for enriching cardiovascular imaging datasets. We introduce a synthetic data generation method, enriched with crucial GT 3D optical flow annotations. We outline the data preparation from a cardiac four-dimensional (4D) CT scan, selection of parameters, and the subsequent creation of synthetic data from the same or other sources of 3D cardiac CT data for training. Our work contributes to overcoming the limitations imposed by the scarcity of high-resolution CT datasets with precise annotations, thereby facilitating the development of accurate and reliable myocardium deformation analysis algorithms for clinical applications and diagnostics. Our code is available at: http://www.github.com/shaharzuler/cardio_volume_skewer