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

Gang WANG, Mingliang ZHOU*, Xin NING, Prayag TIWARI, Haobo ZHU, Guang YANG, Choon Hwai YAP

*Corresponding author for this work

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


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

Original languageEnglish
Article number108282
Number of pages13
JournalComputers in Biology and Medicine
Publication statusPublished - Apr 2024
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 62176027; in part by Ningbo Natural Science Foundation under Grant 2023J280, in part by Ningbo Key R&D Program under Grant 2023Z231, in part by Zhejiang Province Postdoctoral Research Funding Project under Grant ZJ2023008, in part by China Postdoctoral Science Foundation under Grant No. 2023M740741; in part by the Key Projects of Basic Strengthening Plan under Grant 2022-JCJQ-ZD-018-11; in part by the Chongqing Talent under Grant cstc2024ycjh-bgzxm0082; in part by the Joint Equipment Pre Research and Key Fund Project of the Ministry of Education under Grant 8091B012207; in part by the Natural Science Foundation of Chongqing, China under Grant cstc2020jcyj-zdxmX0014; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; and in part by the Guangdong Oppo Mobile Telecommunications Corporation Ltd., under Grant H20221694.


  • Artificial intelligence generation
  • Cardiac ultrasound image
  • Image segmentation
  • Mask learning


Dive into the research topics of 'US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation'. Together they form a unique fingerprint.

Cite this