Local SIMPLE multi-atlas-based segmentation applied to lung lobe detection on chest CT

M. Agarwal; E. A. Hendriks; B. C. Stoel; M. E. Bakker; J. H. C. Reiber; M. Staring

doi:10.1117/12.911552

14 February 2012 Local SIMPLE multi-atlas-based segmentation applied to lung lobe detection on chest CT

M. Agarwal, E. A. Hendriks, B. C. Stoel, M. E. Bakker, J. H. C. Reiber, M. Staring

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831410 (2012) https://doi.org/10.1117/12.911552
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

M. Agarwal, E. A. Hendriks, B. C. Stoel, M. E. Bakker, J. H. C. Reiber, and 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); https://doi.org/10.1117/12.911552

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