Computer-aided Detection in Computed Tomography Colonography with Full Fecal Tagging: Comparison of Standalone Performance of 3 Automated Polyp Detection Systems

AbstractPurposeWe sought to compare the performance of 3 computer-aided detection (CAD) polyp algorithms in computed tomography colonography (CTC) with fecal tagging.MethodsCTC data sets of 33 patients were retrospectively analysed by 3 different CAD systems: system 1, MedicSight; system 2, Colon CAD; and system 3, Polyp Enhanced View. The polyp database comprised 53 lesions, including 6 cases of colorectal cancer, and was established by consensus reading and comparison with colonoscopy. Lesions ranged from 6-40 mm, with 25 lesions larger than 10 mm in size. Detection and false-positive (FP) rates were calculated.ResultsCAD systems 1 and 2 could be set to have varying sensitivities with higher FP rates for higher sensitivity levels. Sensitivities for system 1 ranged from 73%–94% for all lesions (78%–100% for lesions ≥10 mm) and, for system 2, from 64%–94% (78%–100% for lesions ≥10 mm). System 3 reached an overall sensitivity of 76% (100% for lesions ≥10 mm). The mean FP rate per patient ranged from 8–32 for system 1, from 1–8 for system 2, and was 5 for system 3. At the highest sensitivity level for all polyps (94%), system 2 showed a statistically significant lower FP rate compared with system 1 (P = .001). When analysing lesions ≥10 mm, system 3 had significantly fewer FPs than systems 1 and 2 (P < .012).ConclusionsStandalone CTC-CAD analysis in the selected patient collective showed the 3 systems tested to have a variable but overall promising performance with respect to sensitivity and the FP rate

Geodesic tractography segmentation for directional medical image analysis

Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici