Automatic 3D Cardiovascular MR Segmentation with
Densely-Connected Volumetric ConvNets

Lequan Yu1     Jie-Zhi Cheng2    Qi Dou1     Xin Yang1     Hao Chen1     Jing Qin3    Pheng Ann Heng1   

1The Chinese Univeristy of Hong Kong        2Chang Gung University        3The Hong Kong Polytechnic University



Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.



The architecture of our DenseVoxNet. It consists of two DenseBlocks and all operations are implemented in a 3D manner. The green and red color denotes the output of blood pool and myocardium. The graph in left bottom illustrates the dense connectivity scheme taking a 5-layer DenseBlock as an example.



Segmentation results on training images (the first two) and testing images (the last two). The blue and purple color denotes our segmentation results for blood pool and myocardium, respectively, and segmentation ground truth is also presented in white and gray regions in the first two samples.



Snapshot for paper "Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets", MICCAI 2017
Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng Ann Heng



Last update: Dec., 2016