Computer Vision
Shape filtering for false positive reduction at computed tomography colonography
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
An efficient and scalable deformable model for virtual reality-based medical applications
Artificial Intelligence in Medicine
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The success of CT colonography (CTC) depends on appropriate tools for quick and accurate diagnostic reading. Current advancements in computer technology have the potential to bring such tools even to the PC level. In this paper a technique for Computed Aided Diagnosis (CAD) using CT colonography is described. The method labels positions in the volume data, which have a strong likelihood of being polyps and presents them in a user-friendly way. This method will reduce the amount of time needed by the radiologist to make a correct diagnosis. The method was tested on a study group of 18 patients and the sensitivity for polyps of 10 mm or larger was 100%, comparable to that of human readers. The price paid for a high detection rate was a large number of approximately 8 false positive findings per case.