Automatic correction of level set based subvoxel precise centerlines for virtual colonoscopy using the colon outer wall.
Virtual colonoscopy (VC) is becoming a more prevalent method to detect and diagnose colorectal cancer. An essential component of using VC to detect cancerous polyps, especially in conjunction with computer-aided diagnosis, is the accurate calculation of the centerline of the colon. While the colon is often modeled as a simple cylinder, the amount of colonic distention may vary between patients and within the same patient often causing loops and multiple disconnected segments to be present in the colon segmentation. These variations have caused previous centerline algorithms to fail to capture a complete and accurate centerline for all colons. We have developed an automatic method to determine from a computed tomography (CT) VC a subvoxel precise centerline that is accurate even in cases of over-distended or under-distended colons. In this algorithm, the loops in the colon caused by over-distention are detected and removed when the centerline calculation is performed. Also, a newly developed method for the detection and segmentation of the outer wall of the colon is used to connect collapsed portions of the colon where the lumen segmentation fails to produce a continuous centerline. These two methods allow for a complete and accurate centerline to be calculated in uniformly distended colons as well as in colons containing segments which are over-distended and/or under-distended. We have demonstrated successfully the effectiveness of our algorithm on 50 cases, 25 of which resulted in erroneous solutions by previous centerline algorithms due to variability in the colon distention.
Van Uitert
Robert L
RL
National Institutes of Health, Bethesda, MD 20892, USA. uitertr@cc.nih.gov
Summers
Ronald M
RM
D000465:Algorithms; D001185:Artificial Intelligence; D003106:Colon / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000295:instrumentation / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique