14 February 2012 Local SIMPLE multi-atlas-based segmentation applied to lung lobe detection on chest CT
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Abstract
For multi atlas-based segmentation approaches, a segmentation fusion scheme which considers local performance measures may be more accurate than a method which uses a global performance measure. We improve upon an existing segmentation fusion method called SIMPLE and extend it to be localized and suitable for multi-labeled segmentations. We demonstrate the algorithm performance on 23 CT scans of COPD patients using a leave-one- out experiment. Our algorithm performs significantly better (p < 0.01) than majority voting, STAPLE, and SIMPLE, with a median overlap of the fissure of 0.45, 0.48, 0.55 and 0.6 for majority voting, STAPLE, SIMPLE, and the proposed algorithm, respectively.
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M. Agarwal, E. A. Hendriks, B. C. Stoel, M. E. Bakker, J. H. C. Reiber, M. Staring, "Local SIMPLE multi-atlas-based segmentation applied to lung lobe detection on chest CT", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831410 (14 February 2012); doi: 10.1117/12.911552; https://doi.org/10.1117/12.911552
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