Optimal search guided by partial active shape model for prostate segmentation in TRUS images

Automatic prostate segmentation in transrectal ultrasound (TRUS) can be used to register TRUS with magnetic resonance (MR) images for TRUS/MR-guided prostate interventions. However, robust and automated prostate segmentation is challenging due to not only the low signal to noise ratio in TRUS but also the missing boundaries in shadow areas caused by calcifications or hyper-dense prostate tissue. Lack of image information in those areas is a barrier for most existing segmentation methods, which normally leads to user interaction for manual correction. This paper presents a novel method to utilize prior shapes estimated from partial contours to guide an optimal search for prostate segmentation. The proposed method is able to automatically extract prostate boundary from 2D TRUS images without user interaction for correcting shapes in shadow areas. In our approach, the point distribution model was first used to learn shape priors of prostate from manual segmentation results. During segmentation, the missing boundaries in shadow areas are estimated by using a new partial active shape model, which uses partial contour as input but returns complete estimated shape. Prostate boundary is then obtained by using a discrete deformable model with optimal search, which is implemented efficiently by using dynamic programming to produce robust segmentation results. The segmentation of each frame is performed in multi-scale for robustness and computational efficiency. In our experiments of segmenting 162 images grabbed from ultrasound video sequences of 10 patients, the average mean absolute distance was 1.79mm±0.95mm. The proposed method was implemented in C++ based on ITK and took about 0.3 seconds to segment the prostate from a 640x480 image on a Core2 1.86 GHz PC.