Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.Recent FindingsWith the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible.SummaryThe application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.
Source: Current Cardiology Reports - Category: Cardiology Source Type: research