Graph-based active contours using shape priors for prostate segmentation with MRI

Prostate segmentation based on magnetic resonance images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Recently, graph based interactive (semi-automatic) segmentation methods have emerged as a useful substitute to fully automated segmentation for many medical imaging tasks. A small amount of user input often resolves ambiguous decisions on the part of these algorithms. In this study, we propose to use graph-based active contours to segment prostate from a given magnetic resonance image (MRI). Traditional graph-based active contours are typically quite successful for piecewise constant images, but they may fail in cases where magnetic resonance image has diffuse edges, or multiple similar objects (e.g., bladder close to prostate) within close proximity. In order to mitigate these problems, we incorporate a shape prior in our graph-based prostate extraction scheme. Using real world prostate MR images from a well-known database, we show the effectiveness of the proposed method and compare it to results without the shape prior.