Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 323-333.doi: 10.1007/s11390-021-0782-5

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

Seg-CapNet: A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

Yang-Jie Cao1, Member, CCF, Shuang Wu1, Chang Liu1, Nan Lin1, Yuan Wang2, Cong Yang1,*, Member, CCF, and Jie Li1,3, Senior Member, IEEE        

  1. 1 School of Software, Zhengzhou University, Zhengzhou 450000, China;
    2 Center of Modern Analysis and Gene Sequencing, Zhengzhou University, Zhengzhou 450000, China;
    3 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200000, China
  • Received:2020-07-05 Revised:2021-03-09 Online:2021-03-05 Published:2021-04-01
  • Contact: Cong Yang
  • About author:Yang-Jie Cao is currently an associate professor of the School of Software, Zhengzhou University, Zhengzhou. He received his Ph.D. degree in computer science from Xi'an Jiaotong University, Xi'an, in 2012, and his M.S. degree in computer science from Zhengzhou University, Zhengzhou, in 2006. His current research interests include computer vision and intelligent computing, artificial intelligence, and high-performance computing.
  • Supported by:
    This work was supported by the Collaborative Innovation Major Project of Zhengzhou under Grant No. 20XTZX06013, and the National Natural Science Foundation of China under Grant No. 61932014.

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsulebased neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.

Key words: capsule neural network; image segmentation; left ventricle segmentation; cardiac magnetic resonance imaging;

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