A Multi-step Machine Learning Approach for Short Axis MR Images Segmentation

摘要

Segmentation of cardiac magnetic resonance (cMR) images is often the first step necessary to compute common diagnostic biomarkers, such as myocardial mass and left ventricle (LV) ejection fraction. Often image segmentation and analysis require significant, time-consuming user input. Machine learning has been increasingly adopted to automatically segment medical images to lessen the burden on image segmentation and image analysis for model construction and validation. In this work we present a multi-step machine learning approach to segment short axis cMR images based on a heart locator and the weighted average of 2D and 2D++ UNets. The presence of a heart locator led to more accurate results and allowed to increase the neural network training batch size. Finally, the obtained segmentations are post-processed using spline interpolation and the Loop scheme to generate left ventricular endocar-dial and epicardial surfaces at the end of diastole and end of systole.