Automatic segmentation of the prostate on MR images based on anatomy and deep learning

Lei Tao, Ling Ma, Maoqiang Xie, Xiabi Liu, Zhiqiang Tian, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate segmentation of the prostate has many applications in the detection, diagnosis and treatment of prostate cancer. Automatic segmentation can be a challenging task because of the inhomogeneous intensity distributions on MR images. In this paper, we propose an automatic segmentation method for the prostate on MR images based on anatomy. We use the 3D U-Net guided by anatomy knowledge, including the location and shape prior knowledge of the prostate on MR images, to constrain the segmentation of the gland. The proposed method has been evaluated on the public dataset PROMISE2012. Experimental results show that the proposed method achieves a mean Dice similarity coefficient of 91.6% as compared to the manual segmentation. The experimental results indicate that the proposed method based on anatomy knowledge can achieve satisfactory segmentation performance for prostate MRI.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsCristian A. Linte, Jeffrey H. Siewerdsen
PublisherSPIE
ISBN (Electronic)9781510640252
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling - Virtual, Online
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11598
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
CityVirtual, Online
Period2/15/212/19/21

Keywords

  • Anatomy
  • Deep learning
  • Image segmentation
  • Location constraint
  • MRI
  • Prostate
  • Shape prior knowledge

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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