Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Yi Wang, Dong Ni, Haoran Dou, Xiaowei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng Ann Heng, Tianfu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

62 Citations (Scopus)

Abstract

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks.

Original languageEnglish
Article number8698868
Pages (from-to)2768-2778
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume38
Issue number12
DOIs
Publication statusPublished - Dec 2019

Keywords

  • 3D segmentation
  • Attention mechanisms
  • deep features
  • feature pyramid network
  • transrectal ultrasound

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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