A Squeeze Convolutional Network For MRI Right Ventricle Segmentation
Zexiong Liu, Xuan Yang · 2019
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 U-shaped network architecture to extract RV features in the coding layers and make end-to-end 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.