1. Visualization and Detection of Colonic Polyps
Joseph Marino, Arie Kaufman, Krishna Chaitanya Gurijala
Stony Brook University, Stony Brook, NY
Introduction
Virtual Colonoscopy
Colon Flattening
VC-OC Path Correlation
Colorectal cancer is the 2nd leading cause of cancer death in the U.S.
Detection and removal of adenomas is key for prevention and treatment
Virtual colonoscopy (VC) as alternative to traditional screening
Reconstruct virtual models that can be easily interrogated by humans
Computer analysis for the detection of suspicious areas automatically
Ray casting through CT data
Isosurfacing transfer function
Virtual view of colon wall from eye position inside the lumen
More intuitive than 2D CT slices; mimics optical colonoscopy (OC)
Automatic navigation via centerline
Virtually slice colon open (from rectum to cecum)
Conformally map surface to a plane (preserves shapes)
Provide a correlation of the VC and optical colonoscopy (OC) paths
Estimate OC path as nearly hugging corners shortest path
Find corresponding points on paths within colon lumen cross section
VC Centerline
OC Path (estimate)
Computer-Aided Detection
Automatically detect suspicious areas that might contain a polyp
Data preprocessing: digital cleansing segmentation mesh extraction conformal mesh flattening
Use normalized direction vector for each point on the VC centerline
Obtain plane which intersects colon lumen at this point
Find point on estimated OC path which intersects this plane
Compare locations on splines and with simulated VC-OC views
Data
Supine & prone abdomen CT scans
Typically 500 axial slices each
Waste material tagged with barium (appears brightly in scans)
Digitally cleanse waste material
Electronic Biopsy
Translucent transfer function (integration of values along ray)
Map higher density to red and map lower density to blue
Differentiate between adenomas, hyperplastic polyps, and stool
Adenomas have irregular high density structure
1) Render electronic biopsy image on the flattened mesh:
2) Perform clustering on the biopsy image to identify suspicious areas:
Matching Points
Original
Cleansed VC
Simulated OC
Segment colon using air in lumen (black areas in scans)
Generate centerline for navigation
Extract colon wall as triangle mesh
Proposed Work
3) Perform false positive reduction using shape-based analysis only at suspicious areas:
Expand our analysis work for many VC components
Analyze effectiveness of electronic biopsy and possible scoring system
Anatomical feature detection
Perform supine & prone registration
Improve CAD work by integrating supine-prone registration
Results on 188 datasets:
100 % sensitive & 3.2 FP/case
Adenoma (pre-cancerous)
Hyperplastic Polyp
Retained Stool
Supine
Prone