Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph Cuts

We propose a fully automated method for prostate segmentation using random forests (RFs) and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel segmentation, and its subsequent classification using image features and RF classifiers. The VOIs probability map is generated using image and context features, and a second set of RF classifiers. The negative log-likelihood of the probability maps acts as the penalty cost in a second-order Markov random field cost function. Semantic information from the second set of RF classifiers is an important measure of each feature to the classification task, which contributes to formulating the smoothness cost. The cost function is optimized using graph cuts to get the final segmentation of the prostate. With average dice metric (DM) > 0.91 (on the training set) and DM > 0.81 (on the test set), our experimental results show that inclusion of the context and semantic information contributes to higher segmentation accuracy than other methods.