Lung metastases detection in CT images using 3D template matching.

The aim of this study is to demonstrate a novel, fully automatic computer detection method applicable to metastatic tumors to the lung with a diameter of 4-20 mm in high-risk patients using typical computed tomography (CT) scans of the chest. Three-dimensional (3D) spherical tumor appearance models (templates) of various sizes were created to match representative CT imaging parameters and to incorporate partial volume effects. Taking into account the variability in the location of CT sampling planes cut through the spherical models, three offsetting template models were created for each appearance model size. Lung volumes were automatically extracted from computed tomography images and the correlation coefficients between the subregions around each voxel in the lung volume and the set of appearance models were calculated using a fast frequency domain algorithm. To determine optimal parameters for the templates, simulated tumors of varying sizes and eccentricities were generated and superposed onto a representative human chest image dataset. The method was applied to real image sets from 12 patients with known metastatic disease to the lung. A total of 752 slices and 47 identifiable tumors were studied. Spherical templates of three sizes (6, 8, and 10 mm in diameter) were used on the patient image sets; all 47 true tumors were detected with the inclusion of only 21 false positives. This study demonstrates that an automatic and straightforward 3D template-matching method, without any complex training or postprocessing, can be used to detect small lung metastases quickly and reliably in the clinical setting.

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