Dual optimization based prostate zonal segmentation in 3D MR images

Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of for the whole gland (WG), for the CG, and for the PZ in 3D body-coil MR images; for the WG, for the CG, and for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC.