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.

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DOI: http://dx.doi.org/10.15353/vsnl.v2i1.113

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