A Squeeze Convolutional Network For MRI Right Ventricle Segmentation Segmentation of the right ventricle (RV) is difficult due to the variable shape and fuzziness of RV boundaries. In this paper, we propose an effective squeeze Convolutional Network for RV segmentation. A squeeze expand attention (SEA) module is constructed by combining squeezing, expanding, and adaptive weighting of features to generate feature maps for prediction. Our SEA module is introduced into a Ushaped network architecture to extract RV features in the coding layers and make endtoend decisions in the decoding layers. Besides, a loss function constrained by RV shape constraints is proposed in our network to attenuate abnormal prediction results. Our proposed network is with fewer parameters and is rapid in training. Experimental results on public datasets show that our proposed network achieves more accurate segmentation accuracy compared with the existing methods significantly in terms of accuracy and speed.

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.