UNASSIGNED: A study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range 31-90 years) was approved by our institutional review board and evaluated retrospectively. The endoluminal lesions were segmented using gray level threshold. The marching cubes algorithm was used to detect isosurfaces in the segmented volumetric data sets. TGM allows users to interactively apply grey level mapping (GM) to region of interest (ROI) in the 3D CTC. Radiologists conducted the clinical evaluation and the resulting data were analyzed.
UNASSIGNED: TGM and GM are significantly superior to SR in terms of surface texture, 3D shape, the confidence of 3D to 2D, 2D to 3D image correlation, and clinical classification of endoluminal lesions (P < 0.01). The specificity and diagnostic accuracy of GM and TGM methods are significantly better than those of SR (P < 0.01). Moreover, TGM performs better than GM (specificity: 75.0-85.7% vs. 53.6-64.3%; accuracy: 88.7-93.5% vs. 77.4-83.9%). TGM is a preferable display mode for further localization and differentiation of a lesion in CTC navigation.
UNASSIGNED: Compared with only the spatial shape information in traditional SR of CTC images, the 3D shapes and gray level information of endoluminal lesions can be provided by TGM simultaneously. 3D to 2D image correlations are also increased and facilitated at the same time. TGM is less affected by adjacent colon surfaces than GM. TGM serves as a better way to improve the image correlation and differentiation of endoluminal lesions.