Project description: Following the success of 3D image registration algorithms in brain MRI, multi-atlas segmentation (MAS) has become one of the most widespread techniques for segmentation of brain structures in MRI scans. The idea behind atlas-based segmentation is pretty simple: if we have a brain MRI scan with manual segmentations (i.e., an atlas), we can deform (“register”) it to a new, test image we want to analyze, and propagate the manual labels in order to obtain an automated segmentation of the test scan. In MAS, when N>1 atlases are available, we can repeat the procedure N times, and then use label fusion techniques to merge the propagated labelings into a single, more robust estimate of the segmentation. In this project, we will explore some simple MAS/label fusion techniques (majority voting, globally weighted fusion, locally weighted fusion), and apply them to a morphometric study of Alzheimer’s disease.
Xavier Artaechevarria et al., “Combination Strategies in Multi‐Atlas Image Segmentation: Application to Brain MR Data”, TMI 2009
Juan Eugenio Iglesias and Mert Sabuncu: “Multi-Atlas Segmentation of Biomedical Images: A Survey”, MedIA, 2015