MISTO: A Multi-Resolution Deformable Model for Segmentation of Soft-Tissue Organs

We propose a multi-resolution integrated model for the segmentation of soft-tissue organs called MISTO. The model is constructed hierarchically to represent the most significant deformations from the training set as well as to generate representative deformation modes of the organ shapes. The clutter surrounding of the surface points are formulated in terms of an external functional which is also learnt automatically from the training samples. By combining a set of powerful shape models and context constraints, the segmentation process can be carried out very effectively. To avoid the local minimum during model optimization, the deformation strategies are designed such that the portions of the surface for which we have more reliable prior knowledge on their possible deformations are deformed first, followed by deformation on the less informed portions. The experimental and validation results verify that our proposed approaches can be robustly applied to highly deformable anatomies such as soft-tissue organs.