Deep Dual-Stream Convolutional Neural Networks for Cardiac Image Semantic Segmentation

Hengqi HU, Bin FANG, Yuting RAN, Xuekai WEI, Weizhi XIAN, Mingliang ZHOU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review


Cardiac image segmentation is essential when applying biomedical informatics to improve industrial healthcare applications. To extract context and detailed information more efficiently and further improve cardiac image segmentation accuracy, we present a novel deep dual-stream convolutional neural network (CNN) for cardiac image semantic segmentation in this article. We use a body stream and a shape stream, respectively, in this method. First, in the body stream we propose integrating a gated fully fusion module to fuse multilevel features in the encoder and decoder paths. In addition, we integrate a feature aggregation module to extract the multiscale context. Second, in the shape stream, we propose using a gated shape CNN exploiting multilevel context to extract detailed information, such as boundary and shape features. Finally, we apply a multitask loss function to align the predicted masks with the ground truth labels. Our experiments on the public cardiac magnetic resonance image dataset show significant performance in the left and right ventricular cavities and myocardium compared to the state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Early online date15 Feb 2024
Publication statusE-pub ahead of print - 15 Feb 2024

Bibliographical note

Publisher Copyright:


  • Convolutional neural networks (CNNs)
  • Feature extraction
  • Fuses
  • Integrated circuits
  • Logic gates
  • Semantic segmentation
  • Shape
  • Streaming media
  • dual-stream
  • image segmentation
  • semantics


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