Deep learning approach to left ventricular non-compaction measurement

TL;DR

This paper introduces a novel deep learning method using CNNs for automatic segmentation and measurement of left ventricular non-compaction, demonstrating high accuracy and speed, outperforming existing tools in diagnosis.

Contribution

First application of CNNs for LVNC segmentation, achieving rapid and accurate results, with expert validation surpassing previous automatic methods.

Findings

CNNs achieve high segmentation accuracy for LVNC

Inference is extremely fast on CPU and GPU

Expert evaluation confirms superior image quality

Abstract

Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer vision approaches exist for LVNC diagnosis, deep learning-based tools could not be found in the literature. In this paper, a first approach using convolutional neural networks (CNNs) is presented. Four CNNs are trained to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with Hypertrophic cardiomyopathy. Inference results confirm that deep learning-based approaches can achieve excellent results in the diagnosis and measurement of LVNC. The two best CNNs (U-Net and Efficient U-Net B1) perform image segmentation in less than 0.2 s on a CPU and in less than 0.01 s on a GPU. Additionally, a subjective evaluation of the output images with the identified zones is performed by expert cardiologists, with a perfect visual agreement for all the slices, outperforming already existing automatic tools.