Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated graph cuts for brain MRI segmentation
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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We propose a method for simultaneous segmentation of serially acquired magnetic resonance (MR) images. An existing graph-cuts based algorithm is extended and applied to 4-D images. A probabilistic atlas is generated for each baseline scan by intersubject registration of multiple labeled images. The atlases are used for baseline and aligned follow-up images and are combined with an intensity model to define a weighted graph that connects the time-points. A minimal cut on this graph yields the segmentation for all timepoints. The resulting segmentations are consistent over time in boundary regions with weak gray scale definition, but reflect atrophy well where the structure boundary is well defined by MR intensity. The hippocampus was segmented in 568 baseline and follow-up images provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The estimated atrophy rates correctly classified AD patients from controls at a rate of 82% (Atrophy rates: AD 3.85%/y.; MCI 2.31%/y.; controls: 0.85%/y.)