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Journal of Imaging Informatics in Medicine

Published in:

01-02-2011

Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region

Authors: Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo, Ivan De Mitri, Marco Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, Massimo Torsello, Ilaria Zecca, Roberto Bellotti, Sabina Tangaro, Piero Calvini, Niccolò Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, Piernicola Oliva

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2011

Abstract

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent ‘fusion’ between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.

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Title: Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region
Authors: Giorgio De Nunzio
Eleonora Tommasi
Antonella Agrusti
Rosella Cataldo
Ivan De Mitri
Marco Favetta
Silvio Maglio
Andrea Massafra
Maurizio Quarta
Massimo Torsello
Ilaria Zecca
Roberto Bellotti
Sabina Tangaro
Piero Calvini
Niccolò Camarlinghi
Fabio Falaschi
Piergiorgio Cerello
Piernicola Oliva
Publication date: 01-02-2011
Publisher: Springer-Verlag
Published in: Journal of Imaging Informatics in Medicine / Issue 1/2011
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-009-9229-1

At a glance: The STEP trials

Springer Medicine

Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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