Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in MR Images

Abstract

Accurate delineation of the prostate in MR images is an essential step for treatment planning and volume estimation of the organ. Prostate segmentation is a challenging task due to its variable size and shape. Moreover, neighboring tissues have a low-contrast with the prostate. We propose a robust and precise automatic algorithm to define the prostate’s boundaries in MR images in this paper. First, we find the prostate’s ROI by a deep neural network and decrease the input image’s size. Next, a dynamic multi-atlas-based approach obtains the initial segmentation of the prostate. A watershed algorithm improves the initial segmentation at the next stage. Finally, an SSM algorithm keeps the result in the domain of allowable prostate shapes. The quantitative evaluation of 74 prostate volumes demonstrated that the proposed method yields a mean Dice coefficient of [Formula: see text]. In comparison with recent researches, our algorithm is robust against shape and size variations.