TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature




MRI has shown promise in identifying prostate tumors with high sensitivity and specificity for the detection of prostate cancer. Accurate segmentation of the prostate plays a key role various tasks: to accurately localize prostate boundaries for biopsy needle placement and radiotherapy, to initialize multi-modal registration algorithms or to obtain the region of interest for computer-aided detection of prostate cancer. However, manual segmentation during biopsy or radiation therapy can be time consuming and subject to inter- and intra-observer variation. This study's purpose it to develop an automated method to address this technical challenge.


We present an automated multi-atlas segmentation for MR prostate segmentation using patch-based label fusion. After an initial preprocessing for all images, all the atlases are non-rigidly registered to a target image. And then, the resulting transformation is used to propagate the anatomical structure labels of the atlas into the space of the target image. The top L similar atlases are further chosen by measuring intensity and structure difference in the region of interest around prostate. Finally, using voxel weighting based on patch-based anatomical signature, the label that the majority of all warped labels predict for each voxel is used for the final segmentation of the target image.


This segmentation technique was validated with a clinical study of 13 patients. The accuracy of our approach was assessed using the manual segmentation (gold standard). The mean volume Dice Overlap Coefficient was 89.5±2.9% between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D MRI-guided prostate intervention.


We have developed a new prostate segmentation approach based on the optimal feature learning label fusion framework, demonstrated its clinical feasibility, and validated its accuracy. This segmentation technique could be a useful tool in image-guided interventions for prostate-cancer diagnosis and treatment.