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14 February 2012 Manifold learning for atlas selection in multi-atlas-based segmentation of hippocampus
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Alzheimer's disease (AD) severely affects the hippocampus: it loses mass and shrinks as the disease advances. Thus delineation of the hippocampus is an important task in the clinical study of AD. Because of its simplicity and good performance, multi-atlas based segmentation has become a popular approach for medical image segmentation. We propose to use manifold learning for atlas selection in the framework of multi-atlas based segmentation. The framework only benefits when selecting atlases similar to the target image. Since manifold learning assigns each image a coordinate in low-dimensional space by respecting the neighborhood relationship, it is well suited for atlas selection. The key contribution is that we use manifold learning based on a metric derived from non-rigid transformation as the resulting embedding better captures deformations or shape differences between images than similarity measures based on voxel intensity. The proposed method is evaluated in a leave-one-out experiment on a set of 110 hippocampus images; we report mean Dice score of 0.9114 (0.0227). The method was validated against a state-of-the-art method for hippocampus segmentation.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Albert K. Hoang Duc, Marc Modat, Kelvin K Leung, Timor Kadir, and Sébastien Ourselin "Manifold learning for atlas selection in multi-atlas-based segmentation of hippocampus", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140Z (14 February 2012);

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