Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection.

Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model.

Improving a Deep Learning Model to Accurately Diagnose LVNC.

Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult.

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach.

Background And Objective: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness.

Methods: This paper presents the use of a deep learning-based method to measure the (VT%) value and diagnose this rare cardiomyopathy.

A highly accurate method for quantifying LVNC cardiomyophaty.

Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular endo-myocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopthies. Several enhancements are proposed and implemented to improve a software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricular myocardium for a population of patients with LVNC cardiomyopathy (QLVTHC-NC).

Towards an Enhanced Tool for Quantifying the Degree of LV Hyper-Trabeculation.

Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular (LV) endomyocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopathies. Current methods for quantification of LV trabeculae have limitations.