Automated detection and segmentation of large lesions in CT colonography.
Computerized tomographic colonography is a minimally invasive technique for the detection of colorectal polyps and carcinoma. Computer-aided diagnosis (CAD) schemes are designed to help radiologists locating colorectal lesions in an efficient and accurate manner. Large lesions are often initially detected as multiple small objects, due to which such lesions may be missed or misclassified by CAD systems. We propose a novel method for automated detection and segmentation of all large lesions, i.e., large polyps as well as carcinoma. Our detection algorithm is incorporated in a classical CAD system. Candidate detection comprises preselection based on a local measure for protrusion and clustering based on geodesic distance. The generated clusters are further segmented and analyzed. The segmentation algorithm is a thresholding operation in which the threshold is adaptively selected. The segmentation provides a size measurement that is used to compute the likelihood of a cluster to be a large lesion. The large lesion detection algorithm was evaluated on data from 35 patients having 41 large lesions (19 of which malignant) confirmed by optical colonoscopy. At five false positive (FP) per scan, the classical system achieved a sensitivity of 78%, while the system augmented with the large lesion detector achieved 83% sensitivity. For malignant lesions, the performance at five FP/scan was increased from 79% to 95%. The good results on malignant lesions demonstrate that the proposed algorithm may provide relevant additional information for the clinical decision process.
Grigorescu
Simona E
SE
Department of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands. s.e.grigorescu@tudelft.nl
Nevo
Shelly T
ST
Liedenbaum
Marjolein H
MH
Truyen
Roel
R
Stoker
Jaap
J
van Vliet
Lucas J
LJ
Vos
Frans M
FM
D000465:Algorithms; D003110:Colonic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009367:Neoplasm Staging