Fully automatic tumor segmentation of breast ultrasound images with deep learning.

Journal: Journal of applied clinical medical physics
Published Date: Dec 9, 2022

Abstract

BACKGROUND: Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U-Net often predict mass regions for these images. Such false-positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening.

Authors

  • Shuai Zhang
    School of Information, Zhejiang University of Finance and Economics, Hangzhou, China.
  • Mei Liao
    Department of Ultrasound, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Yongyi Zhu
    Department of Ultrasound, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yanling Zhang
    1 School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, P. R. China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Rongqin Zheng
    Guangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Linyang Lv
    Precision Care technology, Hangzhou, China.
  • Dejiang Zhu
    Precision Care technology, Hangzhou, China.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.

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