Deploying deep learning approaches to left ventricular non-compaction measurement

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 and tuned to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with hypertrophic cardiomyopathy. We have evaluated them in the learning phase with an NVIDIA Turing GPU and 2 competitive Xeon CPUs. Inference results confirm that deep learning-based approaches can achieve excellent results in the diagnosis and measurement of LVNC. The final proposal enables real-time analysis of 15-slice MRI studies on both GPU and CPU, obtaining noticeable speed-ups with regard to manual, semi-automatic and fully automatic approaches. 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.