Real-time 3D left ventricle segmentation and ejection fraction using deep learning

Supervised learning for 3D left ventricle (LV) ultrasound segmentation is difficult due to the challenges of acquiring large amounts annotated data. In this work, pre-training on a weakly labeled dataset, combined with augmentations and fine-tuning on a limited dataset using a straightforward 3D convolutional U-net type neural network was investigated. The results indicate that an accuracy close to both state-of-the-art and inter-observer can be achieved with such an approach. The resulting neural network was highly efficient (17 ms on laptop GPU) and was used to create a real-time application for fully automatic LV volume and ejection fraction measurements over multiple heartbeats to enhance practical use in the echo lab.