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Virtual Elastography Ultrasound via Generative Adversarial Network and Its Application to Breast Cancer Diagnosis

In: Generative Machine Learning Models in Medical Image Computing

Author

Listed:
  • Zhao Yao

    (Hunan University, National Engineering Research Center for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering)

  • Yuanyuan Wang

    (Fudan University, Department of Electronic Engineering)

  • Min Liu

    (Hunan University, National Engineering Research Center for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering)

  • Jianqiao Zhou

    (Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Department of Ultrasound)

  • Jinhua Yu

    (Fudan University, Department of Electronic Engineering)

Abstract

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing an improved generative adversarial model (GAN) to synthesize virtual EUS (V-EUS) from conventional B-mode images. Specifically, a bi-discriminator structure and a color prior module are designed to model the intrinsic attributes of the EUS. A total of 4580 cases were collected from 15 medical centers and extensive experiments were designed to demonstrate the validity of the proposed model. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5 % $$\%$$ after V-EUS is equipped.

Suggested Citation

  • Zhao Yao & Yuanyuan Wang & Min Liu & Jianqiao Zhou & Jinhua Yu, 2025. "Virtual Elastography Ultrasound via Generative Adversarial Network and Its Application to Breast Cancer Diagnosis," Springer Books, in: Le Zhang & Chen Chen & Zeju Li & Greg Slabaugh (ed.), Generative Machine Learning Models in Medical Image Computing, chapter 0, pages 149-163, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-80965-1_8
    DOI: 10.1007/978-3-031-80965-1_8
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