A multiple-channel and atrous convolution network for ultrasound image segmentation.

Journal: Medical physics
Published Date: Oct 18, 2020

Abstract

PURPOSE: Ultrasound image segmentation is a challenging task due to a low signal-to-noise ratio and poor image quality. Although several approaches based on the convolutional neural network (CNN) have been applied to ultrasound image segmentation, they have weak generalization ability. We propose an end-to-end, multiple-channel and atrous CNN designed to extract a greater amount of semantic information for segmentation of ultrasound images.

Authors

  • Lun Zhang
    Department of Maxillofacial and Otorhinolaryngology Oncology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Junhua Zhang
    Evidence-based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Zonggui Li
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, 650091, China.
  • Yingchao Song
    School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, 650091, China.

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