Segmentation of the Left Ventricle for the Cardiac Phases between End-Diastole and End-Systole

Automatic segmentation of cardiac structures, including the left ventricle (LV), is a crucial step for evaluating cardiac function. While deep learning is the leading approach for this task, the scarcity of labeled data presents a significant challenge. In scenarios with very limited labeled data, even semi-supervised learning methods may not be effective as they rely on the performance of an initially trained network. In this work, we propose a novel method for segmenting the LV contours for unlabeled cardiac phases between end-diastole (ED) and end-systole (ES). Our method leverages temporal coherence from the LV volume-time and shape information from the labeled cardiac phases to transform the ED and ES ground truth labels to the intervening cardiac phases, resulting in a larger set of labeled data. We evaluate our approach using three methods: 1- visual inspection, 2- using our method as data augmentation for training a CNN with limited ground truths, and 3- comparing results with fully supervised segmentation networks. Our method outperforms in all validation methods. Implementation is available at: "https://github.com/behnam-rahmati/LV-label-propagation".