Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement

Abstract

This paper proposes a method based on the active shape model (ASM) to segment the prostate in magnetic resonance (MR) images. Due to the great variability in appearance among different boundaries of the prostate and among subjects, the traditional ASM is weak in MR image prostate segmentation. To address these limitations, we investigated a novel ASM-based method by incorporating deep feature learning into our previous liver segmentation framework. First, an adaptive feature learning probability boosting tree (AFL-PBT) based on both simple handcrafted features and deep learned features was developed for prostate pre-segmentation and for further shape model initialization. The proposed AFL-PBT classifier also provided a boundary searching band, which made the ASM less sensitive to model initialization. Then, the convolutional neutral network (CNN) deep learning method was used to train a boundary model, which separated voxels into three types: near, inside, and outside the boundary. A three-level ASM based on the CNN boundary model was employed for the final segmentation refinement. On MICCAI PROMISE12 test data sets, the proposed method yielded a mean Dice score of 84% with a standard deviation of 4%. The experimental results demonstrated that the proposed method outperformed other ASM-based prostate MRI segmentation methods and achieved a level of accuracy comparable to that of state-of-the-art methods.