"Automatic Lung Lobe Segmentation of COPD Patients using Iterative B-Spline Fitting"

Denis P. Shamonin, Marius Staring, M. Els Bakker, Changyan Xiao, Jan Stolk, Johan H.C. Reiber and Berend C. Stoel


We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures, removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial for good performance in patient data, containing incomplete and disease-affected fissures.
The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.



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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 = "{Denis P. Shamonin and Marius Staring and M. Els Bakker and Changyan Xiao and Jan Stolk and Johan H.C. Reiber and Berend C. Stoel}",
title = "{Automatic Lung Lobe Segmentation of COPD Patients using Iterative B-Spline Fitting}",
booktitle = "{SPIE Medical Imaging: Image Processing}",
editor = "{D.R. Haynor and S. Ourselin}",
address = "{San Diego, CA, USA}",
series = "{Proceedings of SPIE}",
volume = "{8314}",
pages = "{83140W}",
month = "{February}",
year = "{2012}",

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