title: Discriminative parameter estimation for random walks segmentation.
abstract: The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.
authors: Center for Visual Computing, Ecole Centrale Paris, FR.
authors: Pierre-Yves
authors: PY
authors: Baudin
authors: Center for Visual Computing, Ecole Centrale Paris, FR.
authors: Danny
authors: D
authors: Goodman
authors: Center for Visual Computing, Ecole Centrale Paris, FR.
authors: Puneet
authors: P
authors: Kumrnar
authors: Institute of Myology, Paris, FR.
authors: Noura
authors: N
authors: Azzabou
authors: Institute of Myology, Paris, FR.
authors: Pierre G
authors: PG
authors: Carlier
authors: Center for Visual Computing, Ecole Centrale Paris, FR.
authors: Nikos
authors: N
authors: Paragios
authors: Center for Visual Computing, Ecole Centrale Paris, FR.
authors: M Pawan
authors: MP
authors: Kumar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D018482:Muscle, Skeletal / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity