Deep Learning Segmentation of the Left Ventricle in Structural CMR: Towards a Fully Automatic Multi-scan Analysis

In the past three years, with the novel use of artificial intelligence for medical image analysis, many authors have focused their efforts on defining automatically the ventricular contours in cardiac Cine MRI. The accuracy reached by deep learning methods is now high enough for routine clinical use. However, integration of other cardiac MR sequences that are routinely acquired along with the functional Cine MR has not been investigated. Namely, T1 maps are static T1-based images that encode in each pixel the T1 relaxation time of the tissue, enabling the definition of local and diffuse fibrosis; T2 maps are static T2-based images that highlight excess water (edema) within the muscle; Late Gadolinium Enhancement (LGE) images are acquired 10 min after injection of a contrast agent that will linger in infarct areas. These sequences are acquired in short-axis plane similar to the 2D Cine MRI, and therefore contain similar anatomical features. In this paper we focus on segmenting the left ventricle in these structural images for further physiological quantification. We first evaluate the use of transfer learning from a model trained on Cine data to analyze these short-axis structural sequences. We also develop an automatic slice selection method to avoid over-segmentation which can be critical in scar/fibrosis/edema delineation. We report good accuracy with dice scores around 0.9 for T1 and T2 maps and correlation of the physiological parameters above 0.9 using only 40 scans and executed in less than 15 s on CPU.