A Local ROI-specific Atlas-based Segmentation of Prostate Gland and Transitional Zone in Diffusion MRI

  • Junjie Zhang
  • Sameer Baig
  • Alexander Wong
  • Masoom A. Haider
  • Farzad Khalvati

Abstract

Segmentation of prostate and related anatomic structure, such as transitional zone, in medical images facilitates prostate cancer detection, as well as a number of other clinical practices. In this paper, we propose a semi-automatic local ROI-specific atlas-based segmentation (LABS) method to segment prostate gland and transitional zone in diffusion magnetic resonance images. Inspired by a sequential registration-based segmentation method, the proposed method further reduces the amount of user intervention and focuses on the vicinity of prostate for atlas matching and atlas-to-target registration by specifying the bounding boxes of prostate gland on key slices of volume images. We evaluated the method on an atlas database with the 100 cases by performing a leave-one-out study. Our proposed method produced favorable outcomes with an average Dice similarity coefficient of 0.85±0.03 for prostate gland and 0.77±0.06 for transitional zone segmentations, which indicates the effectiveness of the proposed method and its potential to be used in relevant clinical applications.
Published
2016-10-03
How to Cite
Zhang, J., Baig, S., Wong, A., Haider, M., & Khalvati, F. (2016). A Local ROI-specific Atlas-based Segmentation of Prostate Gland and Transitional Zone in Diffusion MRI. Journal of Computational Vision and Imaging Systems, 2(1). https://doi.org/10.15353/vsnl.v2i1.113
Section
Articles