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NBA篮球预测:?US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation

2024-04-01


Wang, Gang; Zhou,?Mingliang; Ning, Xin; Tiwari, Prayag; Zhu, Haobo; Yang, Guang; Yap, Choon Hwai Source: Computers in Biology and Medicine, v 172, April 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.

? 2024 Elsevier Ltd (64 refs.)




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