Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC y-through videos. A complete review of the colon surface requires full navigation from the rectum to the cecum in antegrade and retrograde directions, which is a tedious task that takes an average of 30 minutes. Crowdsourcing is a technique for non-expert users to perform certain tasks, such as image or video annotation. In this work, we use crowdsourcing for the examination of complete VC y-through videos for polyp annotation by non-experts. The motivation for this is to potentially help the radiologist reach a diagnosis in a shorter period of time, and provide a stronger confirmation of the eventual diagnosis. The crowdsourcing interface includes an interactive tool for the crowd to annotate suspected polyps in the video with an enclosing box. Using our work flow, we achieve an overall polyps-per-patient sensitivity of 87.88% (95.65% for polyps ≥5mm and 70% for polyps <5mm). We also demonstrate the efficacy and effectiveness of a non-expert user in detecting and annotating polyps and discuss their possibility in aiding radiologists in VC examinations.
Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D colon model searching
for colorectal polyps. Though VC is widely recognized as a highly sensitive and specific test for identifying
polyps, one limitation is the reading time, which can take over 30 minutes per patient. Large amounts of the
colon are often devoid of polyps, and a way of identifying these polyp-free segments could be of valuable use in
reducing the required reading time for the interrogating radiologist. To this end, we have tested the ability of
the collective crowd intelligence of non-expert workers to identify polyp candidates and polyp-free regions. We
presented twenty short videos flying through a segment of a virtual colon to each worker, and the crowd was
asked to determine whether or not a possible polyp was observed within that video segment. We evaluated our
framework on Amazon Mechanical Turk and found that the crowd was able to achieve a sensitivity of 80.0% and
specificity of 86.5% in identifying video segments which contained a clinically proven polyp. Since each polyp
appeared in multiple consecutive segments, all polyps were in fact identified. Using the crowd results as a first
pass, 80% of the video segments could in theory be skipped by the radiologist, equating to a significant time
savings and enabling more VC examinations to be performed.
Virtual colonoscopy provides techniques not available in optical colonoscopy, an exciting one being the ability to
perform an electronic biopsy. An electronic biopsy image is created using ray-casting volume rendering of the CT data
with a translucent transfer function mapping higher densities to red and lower densities to blue. The resulting image
allows the physician to gain insight into the internal structure of polyps. Benign tissue and adenomas can be
differentiated; the former will appear as homogeneously blue and the latter as irregular red structures. Although this
technique is now common, is included with clinical systems, and has been used successfully for computer aided
detection, there has so far been no study to evaluate the effectiveness of a physician using electronic biopsy in
determining the pathological state of a polyp. We present here such a study, wherein an experienced radiologist ranked
polyps based on electronic biopsy alone per scan (supine and prone), as well as both combined. Our results show a
correct identification 77% of the time using prone or supine images alone, and 80% accuracy using both. Using ROC
analysis based on this study with one reader and a modest sample size, the combined score is not significantly higher
than using a single electronic biopsy image alone. However, our analysis indicates a trend of superiority for the
combined ranking that deserves a follow-up confirmatory study with a larger sample and more readers. This study
yields hope that an improved electronic biopsy technique could become a primary clinical diagnosis method.
We present a set of tools used to enhance the optical colonoscopy procedure in a novel manner with the aim of
improving both the accuracy and efficiency of this procedure. In order to better present the colon information to the
gastroenterologist performing a conventional (optical) colonoscopy, we undistort the radial distortion of the fisheye view
of the colonoscope. The radial distortion is modeled with a function that converts the fisheye view to the perspective
view, where the shape and size of polyps can be more readily observed. The conversion, accelerated on the graphics
processing unit and running in real-time, calculates the corresponding position in the fisheye view of each pixel on the
perspective image. We also merge our previous work in computer-aided polyp detection for virtual colonoscopy into the
optical colonoscopy environment. The physical colonoscope path in the optical colonoscopy is approximated with the
hugging corner shortest path, which is correlated with the centerline in the virtual colonoscopy. With the estimated
distance that the colonoscope has been inserted, we are able to provide the gastroenterologist with visual cues along the
observation path as to the location of possible polyps found by the detection process. In order to present the information
to the gastroenterologist in a non-intrusive manner, we have developed a friendly user interface to enhance the optical
colonoscopy without being cumbersome, distracting, or resulting in a more lackadaisical inspection by the
gastroenterologist.
This work utilizes a novel pipeline for the computer-aided detection (CAD) of colonic polyps, assisting radiologists in locating polyps when using a virtual colonoscopy system. Our CAD pipeline automatically detects polyps while reducing the number of false positives (FPs). It integrates volume rendering and conformal colon flattening with texture and shape analysis. The colon is first digitally cleansed, segmented, and extracted from the CT dataset of the abdomen. The colon surface is then mapped to a 2D rectangle using conformal mapping. Using this colon flattening method, the CAD problem is converted from 3D into 2D. The flattened image is rendered using a direct volume rendering of the 3D colon dataset with a translucent transfer function. Suspicious polyps are detected by applying a clustering method on the 2D volume rendered image. The FPs are reduced by analyzing shape and texture features of the suspicious areas detected by the clustering step. Compared with shape-based methods, ours is much faster and much more efficient as it avoids computing curvature and other shape parameters for the whole colon wall. We tested our method with 178 datasets and found it to be 100% sensitive to adenomatous polyps with a low rate of FPs. The CAD results are seamlessly integrated into a virtual colonoscopy system, providing the radiologists with visual cues and likelihood indicators of areas likely to contain polyps, and allowing them to quickly inspect the suspicious areas and further exploit the flattened colon view for easy navigation and bookmark placement.
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