CT prostate deformable segmentation by boundary regression

Automatic and accurate prostate segmentation from CT images is challenging due to low image contrast, uncertain organ motion, and variable organ appearance in different patient images. To deal with these challenges, we propose a new prostate boundary detection method with a boundary regression strategy for prostate deformable segmentation. Different from the previous regression-based segmentation methods, which train one regression forest for each specific point (e.g., each point on a shape model), our method learns a single global regression forest to predict the nearest boundary points from each voxel for enhancing the entire prostate boundary. The experimental results show that our proposed boundary regression method outperforms the conventional prostate classification method. Compared with other state-of-the-art methods, our method also shows a competitive performance.