4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECGgated cardiac CT, a series of singlechannel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define a sparse loss function on available labels to allow the network to leverage unlabeled images during training and generate a fully segmented sequence. We investigate the accuracy of the proposed 4D network to predict temporally consistent segmentations and compare with traditional 3D segmentation approaches. We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA (video: https:drive.google.comuc?id1nGJX5nviVs8R7tque2zy2uHFcN_Ogn1.).

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 CNNbased method to obtain a 3D representation of LV by integrating information from 2D shortaxis and horizontal and vertical longaxis 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 wellknown CNN architectures for medical image segmentation: UNet and DeepMedic. Fivefold cross validation on a dataset of 20 patients achieved a correlation of 95.093.1(%) for quantification of enddiastolic volume, 91.690.8(%) for endsystolic volume and 80.584.5(%) for EF for the two architectures respectively. We show that (1) incorporating longaxis 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.