"Local SIMPLE Multi Atlas-Based Segmentation Applied to Lung Lobe Detection on Chest CT"

Maruti Agarwal, M. Els Bakker, Emile A. Hendriks, Berend C. Stoel, Johan H.C. Reiber and Marius Staring


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 leaveone-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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Copyright © 2012 by the authors. Published version © 2012 by SPIE. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.


BibTeX entry

author = "{Maruti Agarwal and M. Els Bakker and Emile A. Hendriks and Berend C. Stoel and Johan H.C. Reiber and Marius Staring}",
title = "{Local SIMPLE Multi Atlas-Based Segmentation Applied to Lung Lobe Detection on Chest CT}",
booktitle = "{SPIE Medical Imaging: Image Processing}",
editor = "{D.R. Haynor and S. Ourselin}",
address = "{San Diego, California, USA}",
series = "{Proceedings of SPIE}",
volume = "{8314}",
pages = "{831410}",
month = "{February}",
year = "{2012}",

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