Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.
SEGMENTATION OF MEDICAL IMAGES