Hybrid decision forests for prostate segmentation in multi-channel MR images

We propose a fully automatic learning-based multi-atlas approach to segment the prostate using multi-channel (T1 and T2) MR images. After affine transformation to the template space, multi-scale features are extracted and separate random forest classifiers are learnt for the prostate region from the most similar T1 and T2 atlases. The probabilities from these two classifiers (T1 and T2) are then fused to obtain a robust probabilistic atlas. Finally, using the probabilistic representation for each voxel, the multi-image graph cuts algorithm is applied on these multi-channel images simultaneously to get the final segmentation. The novelty of the proposed method lies in the use of multi-channel MR images, a decision forest learnt from only the most similar MR images, and the fusion of global and local template-based classifiers for prostate segmentation. We apply this method to a set of 107 prostate images, with 77 randomly selected images used for training and the remaining 30 images for testing. The results are compared to the radiologist's labeled ground truth using cross-validation. The best result is obtained via hybrid approach in which the global classifier trained on T1 images and local template-based classifiers trained on T2 images are fused to obtain the final probability for each voxel. Our results indicate that the proposed method is robust, capable of producing accurate segmentation automatically and most importantly, not patient-specific.