9 March 2011 Improving the channeler ant model for lung CT analysis
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Abstract
The Channeler Ant Model (CAM) is an algorithm based on virtual ant colonies, conceived for the segmentation of complex structures with different shapes and intensity in a 3D environment. It exploits the natural capabilities of virtual ant colonies to modify the environment and communicate with each other by pheromone deposition. When applied to lung CTs, the CAM can be turned into a Computer Aided Detection (CAD) method for the identification of pulmonary nodules and the support to radiologists in the identification of early-stage pathological objects. The CAM has been validated with the segmentation of 3D artificial objects and it has already been successfully applied to the lung nodules detection in Computed Tomography images within the ANODE09 challenge. The model improvements for the segmentation of nodules attached to the pleura and to the vessel tree are discussed, as well as a method to enhance the detection of low-intensity nodules. The results on five datasets annotated with different criteria show that the analytical modules (i.e. up to the filtering stage) provide a sensitivity in the 80 - 90% range with a number of FP/scan of the order of 20. The classification module, although not yet optimised, keeps the sensitivity in the 70 - 85% range at about 10 FP/scan, in spite of the fact that the annotation criteria for the training and the validation samples are different.
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Piergiorgio Cerello, Piergiorgio Cerello, Ernesto Lopez Torres, Ernesto Lopez Torres, Elisa Fiorina, Elisa Fiorina, Chiara Oppedisano, Chiara Oppedisano, Cristiana Peroni, Cristiana Peroni, Raul Arteche Diaz, Raul Arteche Diaz, Roberto Bellotti, Roberto Bellotti, Paolo Bosco, Paolo Bosco, Niccolo Camarlinghi, Niccolo Camarlinghi, Andrea Massafra, Andrea Massafra, "Improving the channeler ant model for lung CT analysis", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633A (9 March 2011); doi: 10.1117/12.878310; https://doi.org/10.1117/12.878310
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