GUDU: Geometrically-constrained Ultrasound Data augmentation in U-Net for echocardiography semantic segmentation

Abstract

Echocardiography is a very important medical examination that helps in the computation of critical heart functions. Boundary identification, segmentation and estimation of the volume of key parts of the heart, especially the left ventricle, is a difficult and time-consuming process, even for the most experienced cardiologists. In recent years, research has focused on the automatic segmentation of heart through artificial intelligence techniques and especially with the use of deep learning. Our work is part of this framework.We implemented an ensemble of convolutional neural networks based on the U-net architecture, trained it using a public dataset of cardiac ultrasound images, and combined the outcomes to extract the areas of the left ventricle, myocardium and left atrium. In order to optimize the training process, we have developed a significant data augmentation method based on medical practice. Furthermore, we extended the Dice loss function by imposing additional mandatory anatomical constraints. An ablation study highlights the contribution of each of our proposed modules.The evaluation of our method showed an overall improvement in segmentation accuracy but also in the estimation of clinical metrics. Specifically, using the Dice coefficient for geometric metrics, we achieved for the epicardium a score of 0.96 and 0.955 for the end-diastolic and end-systolic phase respectively. For the clinical metrics of the left ventricle volume, the Pearson correlation coefficient was used where our method gave 0.977, 0.981, 0.897 for the end-diastolic, end-systolic phase and ejection fraction respectively. Scores which up until the writing of this article outperform competitive methods.