US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation.

Journal: Computers in biology and medicine
Published Date: Mar 15, 2024

Abstract

Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often demands a large dataset and expert annotations, resulting in high costs for deep learning algorithms. To address this, our study presents a framework utilizing artificial intelligence generation technology to produce multi-class RGB masks for cardiac US image segmentation. The proposed approach directly performs semantic segmentation of the heart's main structures in US images from various scanning modes. Additionally, we introduce a novel learning approach based on conditional generative adversarial networks (CGAN) for cardiac US image segmentation, incorporating a conditional input and paired RGB masks. Experimental results from three cardiac US image datasets with diverse scan modes demonstrate that our approach outperforms several state-of-the-art models, showcasing improvements in five commonly used segmentation metrics, with lower noise sensitivity. Source code is available at https://github.com/energy588/US2mask.

Authors

  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Mingliang Zhou
    Key Laboratory of Geotechnical and Underground Engineering, Department of Geotechnical Engineering, Tongji University, Siping Road 1239, Shanghai 200092, China.
  • Xin Ning
    Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China; School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing, 100083, China; Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing, 100083, China. Electronic address: ningxin@semi.ac.cn.
  • Prayag Tiwari
    Department of Information Engineering, University of Padova, Italy. Electronic address: prayag.tiwari@dei.unipd.it.
  • Haobo Zhu
    University of Oxford, Oxford, UK.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Choon Hwai Yap
    Department of Bioengineering, Imperial College London, London, UK.

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