A supervoxel-based segmentation method for prostate MR images

• Authors: Zhiqiang Tian, Emory University; Lizhi Liu, Emory University; Baowei Fei, Emory University
• Proceedings Title: MEDICAL IMAGING 2015: IMAGE PROCESSING
• Conference Name: Conference on Medical Imaging - Image Processing
• Publisher: SPIE-INT SOC OPTICAL ENGINEERING
• Conference Place: Orlando, FL
• Volume/Issue: Volume 9413
• Publication Date: 2015-01-01
• Type of Work: Conference | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1117/12.2082255
• Keywords: Science & Technology; Physical Sciences; Life Sciences & Biomedicine; Optics; Radiology, Nuclear Medicine & Medical Imaging; Magnetic resonance imaging (MRI); prostate cancer; segmentation; supervoxel; 3D graph cut; 3D level set; THERMAL ABLATION; GRAPH CUTS; CANCER; REGISTRATION

Abstract

Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a "Supervoxel" based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A threedimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%±3.2%. The segmentation method can be used not only for the prostate but also for other organs.