Sarang Lakare, Dongqing
Chen, Lihong Li, Arie Kaufman, Mark Wax, and
colonoscopy aims to provide a safe and comfortable technique
to examine the colon for polyps (cancerous growth).
In theory, an accurate diagnosis requires complete coverage
of 100% of the lumen surface. Mori et al. presented
a method to track observed regions and present unobserved
areas to the user (SPIE 4321 pp 134-145). We have developed
a similar method that automatically displays, places
in sized order and allows viewing of the areas of the
colon surface not visualized during initial navigational
viewing. While complete surface visualization is possible,
we demonstrate that all of these "missed patches"
do not have to be reviewed to detect clinically significant
Breath-hold, supine and prone spiral CT scans
were performed on 103 patients after bowel preparation
and colon distention with 2L of CO2. After automatic
segmentation and electronic cleansing of the colon lumen,
the medial axis (centerline) was extracted. Volume rendering
fly-through was performed and visualized surfaces were
marked taking into account what Mori termed the "physical
factor" and the "psychological factor".
Instead of only using Mori's description of a marked
single voxel per ray, specifically the one with a opacity
above a certain threshold, our system marks all voxels
once the opacity passes a minimum threshold until it
passes the maximum threshold. This more closely resembles
how voxels contribute to the image in volume rendering.
Also, we incorporate a "psychological factor"
that allows including voxels within 6 centimeters of
the viewpoint to be classified as visualized. In this
application, colon surfaces far away from the virtual
viewpoint will not be counted since the user cannot
adequately analyze those areas. The virtual camera was
passed both antegrade and retrograde, and the combined
visible surface voxel count was recorded. After both
fly-throughs, all "patches" of connected surface
area not yet seen were identified, measured, sorted
by size, and counted. Clinically significant patches
* defined as smallest diameter being 35mm * were sequentially
visualized by stepping through the sorted list, followed
by all patches. The total visualized surface was recorded
Virtual fly through in one
direction viewed an average of 80% (range 61-91) of
the colon surface, primarily missing the backsides of
haustral folds and around sharp bends. Combined antegrade
and retrograde flythroughs viewed an average of 94%
(range 84-99) of the surface. Even with 94% average
coverage, there was an average of 40 (range 13-91) clinically
significant patches not yet visualized. After identifying
and viewing these areas, 98-100% of the lumen surface
was seen. Even if all patches, regardless of size, could
be viewed to achieve 100% surface coverage, this extra
effort would be unnecessary to detect clinically significant,
larger than 5mm, polyps.
New or breakthrough
work: Compared to previous
approaches, this work more accurately marks visualized
surfaces when performing volume rendering based navigation.
It also takes into account psychological factors of
the viewer to not include areas far away from the viewpoint.
A new method of automatically identifying and visualizing
the "missed patches" that sorts them according
to size allows the reviewer to step through the "clinically
significant" patches, thus saving interpretation
By enabling endoscopic navigation along the colon
centerline, in both antegrade and retrograde directions,
virtual colonoscopy allows evaluation behind haustral
folds and around sharp bends, thereby visualizing significantly
more surface area than optical colonoscopy. However,
this still leaves a considerable number of areas unseen.
Automatically marking the visualized surface area and
identifying and viewing unseen patches allow examination
of all clinically significant surfaces of the colon.
Virtual Endosocopy, Volume
Navigation, Complete Surface Coverage
State University of New York at Stony Brook
62 Washington St.
East Setauket, NY 11733
Phone: (631) 444-6576
Fax: (631) 444-9701
Kevin is a Ph.D. candidate in Computer Science at SUNY
Stony Brook. His research interests include
Hardware Architectures for Volume Visualization and