Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study

Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of colon cancer. In this work, we propose a colon wall evolution algorithm polyp enhancing level sets (PELS) based on the level-set formulation that regularizes and enhan...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2007-12, Vol.26 (12), p.1649-1656
Main Authors: Konukoglu, E., Acar, B., Paik, D.S., Beaulieu, C.F., Rosenberg, J., Napel, S.
Format: Article
Language:English
Subjects:
Algorithms
Anatomy, Cross-Sectional - methods
Cancer detection
Colon
Colon - diagnostic imaging
Colon - pathology
Colonic polyps
Colonic Polyps - diagnostic imaging
Colonic Polyps - pathology
Colonography
Colonography, Computed Tomographic - methods
Colorectal cancer
Computed tomography
Computer aided design
computer aided detection (CAD)
CT colonography
Evolution
Humans
Level set
level-sets
Pattern Recognition, Automated - methods
Pilot Projects
Pilots
polyp detection
polyp enhancement
Precursors
Preprocessing
Radiographic Image Interpretation, Computer-Assisted - methods
Sensitivity and Specificity
Shape
Surface fitting
Tomography
Virtual colonoscopy
X-ray imaging
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Summary:Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of colon cancer. In this work, we propose a colon wall evolution algorithm polyp enhancing level sets (PELS) based on the level-set formulation that regularizes and enhances polyps as a preprocessing step to CTC CAD algorithms. The underlying idea is to evolve the polyps towards spherical protrusions on the colon wall while keeping other structures, such as haustral folds, relatively unchanged and, thereby, potentially improve the performance of CTC CAD algorithms, especially for smaller polyps. To evaluate our methods, we conducted a pilot study using an arbitrarily chosen CTC CAD method, the surface normal overlap (SNO) CAD algorithm, on a nine patient CTC data set with 47 polyps of sizes ranging from 2.0 to 17.0 mm in diameter. PELS increased the maximum sensitivity by 8.1% (from to ) for small polyps of sizes ranging from 5.0 to 9.0 mm in diameter. This is accompanied by a statistically significant separation between small polyps and false positives. PELS did not change the CTC CAD performance significantly for larger polyps.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2007.901429