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 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% 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.
Robarts Research Institute, University of Western Ontario, London, ON, Canada. Electronic address: wqiu@robarts.ca.
Wu
W
Qiu
Robarts Research Institute, University of Western Ontario, London, ON, Canada.
Jing
J
Yuan
Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada.
Eranga
E
Ukwatta
Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada.
Yue
Y
Sun
Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada.
Martin
M
Rajchl
Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada; Medical Biophysics, University of Western Ontario, London, ON, Canada.
Aaron
A
Fenster
D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011467:Prostate / Q000033:anatomy & histology* / Q000473:pathology; D011471:Prostatic Neoplasms / Q000473:pathology
3D prostate MRI; Convex optimization; Multi-region segmentation; Zonal segmentation