3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial Context

Accurate left ventricular (LV) segmentation in cardiac MRI facilitates quantification of clinical parameters such as LV volume and ejection fraction (EF). We present a CNN-based method to obtain a 3D representation of LV by integrating information from 2D short-axis and horizontal and vertical long-axis images. Our CNN is flexible to the number of input slices and uses an additional input of image coordinates as spatial context. This concept is validated on variations of two well-known CNN architectures for medical image segmentation: U-Net and DeepMedic. Five-fold cross validation on a dataset of 20 patients achieved a correlation of 95.0/93.1\(\%\) for quantification of end-diastolic volume, 91.6/90.8\(\%\) for end-systolic volume and 80.5/84.5\(\%\) for EF for the two architectures respectively. We show that (1) incorporating long-axis data improves segmentation performance and (2) providing spatial context by adding image coordinates as input to the CNN yields similar performance with a smaller receptive field.