Automatic lung segmentation in CT images with accurate handling of the hilar region.

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.

The MAGIC-5 project: Medical Applications on a Grid Infrastructure Connection.

The main purpose of the MAGIC-5 collaboration is the development of Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of a GRID Infrastructure Connection. A prototype of the system, based on the AliEn GRID Services is already available with a central Server running common services and several clients connecting to it. It has been already successfully used for applications in mammography together with a specific CAD developed within the collaboration. Applications to the case of malignant nodule detection in lung CT scans are now being implemented, while a use of the GRID services is also being applied to PET image analysis aiming at early Alzheimer disease. One of the future prospect of our project is the migration from AliEn to the EGEE/gLite middleware which is likely to become a European standard and will certainly provide more sophisticated tools with respect to the present AliEn functionality. In this work the status of the project and its future prospects will be given, with particular attention to the data management and processing aspects. Medical applications carried on by the collaboration will be also described together with the analysis of the results so far obtained.