Fully-automated segmentation of the striatum in the PET/MR images using data fusion

Different imaging modalities sample different properties of the tissue, and thus the tissue may appear different depending on the imaging technique. As a consequence, the shapes of organs and homogenous regions in tissues often have different shapes depending on the type of imaging. This presents a problem for ROI-based multi-modality quantitative imaging studies, since it is not clear what modality should be used for data segmentation. An example of such study is the quantitative PET imaging of Parkinson's disease subjects, which often present functional atrophy without an anatomical atrophy. A choice must be made between anatomical (MRI) and radioactivity-based (PET) ROIs. In addition manual ROI placement can be very time consuming and may lack consistency. In this work, we propose a new approach to multi-modality image segmentation. The proposed method generates so-called mixed ROIs that can be computed in a fully automated mode from single modality-based pure ROIs. The computation of the mixed ROIs is based on the fusion of probability images. The use of the fusion principles made it possible to transition between the pure ROI shapes in a smooth fashion. The mixed ROIs were found to be better aligned with the high activity regions than the pure MR ROIs, and had higher anatomical fidelity compared to the pure PET ROIs. Using the method, it is possible to generate a multitude of ROI sets for a particular study starting from one or more previously defined regions.