Automated Full Quantification of Left Ventricle with Deep Neural Networks

Abstract

Accurate cardiac left ventricle (LV) quantification is among the most clinically important and most frequently demanded tasks for identification and diagnosis of cardiac diseases and is of great interest in the research community of medical image analysis. However, it is still a task of great challenge due to the high variability of cardiac structure across subjects and the complexity of temporal dynamics of cardiac sequences. Full quantification of cardiac LV includes simultaneously quantifying, for every frame in the whole cardiac cycle, multiple types of cardiac indices, such as cavity and myocardium areas, regional wall thicknesses, LV dimension and cardiac phase. Accurate quantification of these indices will support comprehensive global and regional cardiac function assessment. In this paper, we propose a newly designed multitask learning network which combines the segmentation task and the cardiac indices quantification task. It comprises a segmentation network based on the U-net for image representation, and then followed by a simple convolutional neural network for feature extraction of cardiac indices, two parallel recurrent neural network models are then added for temporal dynamic modeling. Then we use multitask learning to capture the existing correlations among different tasks. Experiments of 5-fold validation results show that the proposed framework achieves high accurate prediction, with average mean absolute error of 173 mm$^{2}$, 2.44 mm, 1.37 mm for areas, dimensions, RWT and phase error rate 7.8%.