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Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease

 
: Kohlmann, P.; Strehlow, J.; Jobst, B.; Krass, S.; Kuhnigk, J.-M.; Anjorin, A.; Sedlaczek, O.; Ley, S.; Kauczor, H.-U.; Wielpütz, M.O.

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International journal of computer assisted radiology and surgery 10 (2015), Nr.4, S.403-417
ISSN: 1861-6410
ISSN: 1861-6429
Englisch
Zeitschriftenaufsatz
Fraunhofer MEVIS ()

Abstract
Purpose
A novel fully automatic lung segmentation method for magnetic resonance (MR) images of patients with chronic obstructive pulmonary disease (COPD) is presented. The main goal of this work was to ease the tedious and time-consuming task of manual lung segmentation, which is required for region-based volumetric analysis of four-dimensional MR perfusion studies which goes beyond the analysis of small regions of interest.
Methods
The first step in the automatic algorithm is the segmentation of the lungs in morphological MR images with higher spatial resolution than corresponding perfusion MR images. Subsequently, the segmentation mask of the lungs is transferred to the perfusion images via nonlinear registration. Finally, the masks for left and right lungs are subdivided into a user-defined number of partitions. Fourteen patients with two time points resulting in 28 perfusion data sets were available for the preliminary evaluation of the developed methods.
Results
Resulting lung segmentation masks are compared with reference segmentations from experienced chest radiologists, as well as with total lung capacity (TLC) acquired by full-body plethysmography. TLC results were available for thirteen patients. The relevance of the presented method is indicated by an evaluation, which shows high correlation between automatically generated lung masks with corresponding ground-truth estimates.
Conclusion
The evaluation of the developed methods indicates good accuracy and shows that automatically generated lung masks differ from expert segmentations about as much as segmentations from different experts.

: http://publica.fraunhofer.de/dokumente/N-301601.html