3D knowledge-based segmentation using pose-invariant higher-order graphs.

Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.

Left ventricle segmentation using diffusion wavelets and boosting.

We propose a method for the segmentation of medical images based on a novel parameterization of prior shape knowledge and a search scheme based on classifying local appearance. The method uses diffusion wavelets to capture arbitrary and continuous interdependencies in the training data and uses them for an efficient shape model. The lack of classic visual consistency in complex medical imaging data, is tackled by a manifold learning approach handling optimal high-dimensional local features by Gentle Boosting. Appearance saliency is encoded in the model and segmentation is performed through the extraction and classification of the corresponding features in a new data set, as well as a diffusion wavelet based shape model constraint. Our framework supports hierarchies both in the model and the search space, can encode complex geometric and photometric dependencies of the structure of interest, and can deal with arbitrary topologies. Promising results are reported for heart CT data sets, proving the impact of the soft parameterization, and the efficiency of our approach.