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11 March 2008 Adaptive local multi-atlas segmentation: application to heart segmentation in chest CT scans
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Abstract
Atlas-based segmentation is a popular generic technique for automated delineation of structures in volumetric data sets. Several studies have shown that multi-atlas based segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on large volumetric data is too time-consuming for routine clinical use. We propose a generally applicable adaptive local multi-atlas segmentation method (ALMAS) that locally decides how many and which atlases are needed to segment a target image. Only the selected parts of atlases are registered. The method is iterative and automatically stops when no further improvement is expected. ALMAS was applied to segmentation of the heart on chest CT scans and compared to three existing atlas-based methods. It performed significantly better than single-atlas methods and as good as multi-atlas methods at a much lower computational cost.
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Eva M. van Rikxoort, Ivana Isgum, Marius Staring, Stefan Klein, and Bram van Ginneken "Adaptive local multi-atlas segmentation: application to heart segmentation in chest CT scans", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691407 (11 March 2008); https://doi.org/10.1117/12.772301
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