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The Random Walks (RW) algorithm is one of the most e - cient and easy-to-useprobabilistic segmentation methods. By combining contrast terms with priorterms, it provides accurate segmentations of medical images in a fullyautomated manner. However, one of the main drawbacks of using the RW algorithmis that its parameters have to be hand-tuned. we propose a novel discriminativelearning framework that estimates the parameters using a training dataset. Themain challenge we face is that the training samples are not fully supervised.Speci cally, they provide a hard segmentation of the images, instead of aproba- bilistic segmentation. We overcome this challenge by treating the opti-mal probabilistic segmentation that is compatible with the given hardsegmentation as a latent variable. This allows us to employ the latent supportvector machine formulation for parameter estimation. We show that our approachsigni cantly outperforms the baseline methods on a challenging datasetconsisting of real clinical 3D MRI volumes of skeletal muscles.

Discriminative Parameter Estimation for Random Walks Segmentation