Mr. Nicolas Sezille Profile

Nicolas Sezille

at Dublin City Univ

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Proceedings Article | 19 March 2003 Paper

Automated synthesis, insertion, and detection of polyps for CT colonography

Nicolas Sezille, Robert Sadleir, Paul Whelan

Proceedings Volume 4877, (2003) https://doi.org/10.1117/12.463718

KEYWORDS: Colon, Detection and tracking algorithms, Virtual colonoscopy, Colorectal cancer, 3D modeling, Computed tomography, Image segmentation, Shape analysis, Binary data, Distance measurement

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CT Colonography (CTC) is a new non-invasive colon imaging technique which has the potential to replace conventional colonoscopy for colorectal cancer screening. A novel system which facilitates automated detection of colorectal polyps at CTC is introduced. As exhaustive testing of such a system using real patient data is not feasible, more complete testing is achieved through synthesis of artificial polyps and insertion into real datasets. The polyp insertion is semi-automatic: candidate points are manually selected using a custom GUI, suitable points are determined automatically from an analysis of the local neighborhood surrounding each of the candidate points. Local density and orientation information are used to generate polyps based on an elliptical model. Anomalies are identified from the modified dataset by analyzing the axial images. Detected anomalies are classified as potential polyps or natural features using 3D morphological techniques. The final results are flagged for review. The system was evaluated using 15 scenarios. The sensitivity of the system was found to be 65% with 34% false positive detections. Automated diagnosis at CTC is possible and thorough testing is facilitated by augmenting real patient data with computer generated polyps. Ultimately, automated diagnosis will enhance standard CTC and increase performance.

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