The large size of the datasets produced by medical imaging protocols contributes to the success of supervised discriminative methods for semantic labelling of images. Our study makes use of a general and efficient emerging framework, discriminative random forests, for the detection of brain lesions in multi-modal magnetic resonance images (MRIs). The contribution is three-fold. First, we focus on segmentation of brain lesions which is an essential task to diagnosis, prognosis and therapy planning. A context-aware random forest is designed for the automatic multi-class segmentation of MS lesions, low grade and high grade gliomas in MR images. It uses multi-channel MRIs, prior knowledge on tissue classes, symmetrical and long-range spatial context to discriminate lesions from background. Then, we investigate the promising perspective of estimating the brain tumor cell density from MRIs. A generative-discriminative framework is presented to learn the latent and clinically unavailable tumor cell density from model-based estimations associated with synthetic MRIs. The generative model is a validated and publicly available biophysiological tumor growth simulator. The discriminative model builds on multi-variate regression random forests to estimate the voxel-wise distribution of tumor cell density from input MRIs. Finally, we present the “Spatially Adaptive Random Forests” which merge the benefits of multi-scale and random forest methods and apply it to previously cited classification and regression settings. Quantitative evaluation of the proposed methods are carried out on publicly available labeled datasets and demonstrate state of the art performance.
