Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas-based segmentation is used on a high-resolution mono-modal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework in order to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the high-resolution mono-modal image by atlas-based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas-registration approach including multi-scale tumor-growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning.