Deep learning approach to left ventricular non-compaction measurement
This work addresses the need for automated and accurate LVNC diagnosis and measurement for cardiologists, offering an incremental improvement over existing computer vision methods.
This paper introduces the first deep learning approach for measuring Left Ventricular Non-Compaction (LVNC) by training four CNNs to segment compacted and trabecular areas of the left ventricle. The U-Net and Efficient U-Net B1 models achieved excellent segmentation performance, with inference times under 0.2s on CPU and 0.01s on GPU, and perfect visual agreement from expert cardiologists, outperforming existing automatic tools.
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.