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Authors: Nicola Giuliani 1 ; Christian Payer 2 ; Michael Pienn 1 ; Horst Olschewski 3 and Martin Urschler 4

Affiliations: 1 Ludwig Boltzmann Institute for Lung Vascular Research, Austria ; 2 Institute of Computer Graphics and Vision and University of Technology, Austria ; 3 Medical University of Graz, Austria ; 4 Institute of Computer Graphics and Vision, University of Technology and Ludwig Boltzmann Institute for Clinical Forensic Imaging, Austria

Keyword(s): Lung Lobe Segmentation, Discrete Optimization, Graph Cuts, Alpha-Expansion.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: Fully-automatic lung lobe segmentation in pathological lungs is still a challenging task. A new approach for automatic lung lobe segmentation is presented based on airways, vessels, fissures and prior knowledge on lobar shape. The anatomical information and prior knowledge are combined into an energy equation, which is minimized via graph cuts to yield an optimal segmentation. The algorithm is quantitatively validated on an in-house dataset of 25 scans and on the LObe and Lung Analysis 2011 (LOLA11) dataset, which contains a range of different challenging lungs (total of 55) with respect to lobe segmentation. Both experiments achieved solid results including a median absolute distance from manually set fissure markers of 1.04mm (interquartile range: 0.88-1.09mm) on the in-house dataset and a score of 0.866 on the LOLA11 dataset. We conclude that our proposed method is robust even in case of pathologies.

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Paper citation in several formats:
Giuliani, N.; Payer, C.; Pienn, M.; Olschewski, H. and Urschler, M. (2018). Pulmonary Lobe Segmentation in CT Images using Alpha-Expansion. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 387-394. DOI: 10.5220/0006624103870394

@conference{visapp18,
author={Nicola Giuliani. and Christian Payer. and Michael Pienn. and Horst Olschewski. and Martin Urschler.},
title={Pulmonary Lobe Segmentation in CT Images using Alpha-Expansion},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={387-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006624103870394},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Pulmonary Lobe Segmentation in CT Images using Alpha-Expansion
SN - 978-989-758-290-5
IS - 2184-4321
AU - Giuliani, N.
AU - Payer, C.
AU - Pienn, M.
AU - Olschewski, H.
AU - Urschler, M.
PY - 2018
SP - 387
EP - 394
DO - 10.5220/0006624103870394
PB - SciTePress

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