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.

Prior to the study we report here it had been thought that the sole undisturbed nighttime mechanisms for producing CO•. vibrationally excited in the v3 mode were (1) vibrational excitation by thermal collisions and (2) absorption of 4.3-um earthshine by CO•.. In this paper we show detailed evidence that the mechanism OH(v) + N•. • OH(v -1) + N•. •, followed by N•." + CO•. -• N•. + CO4v0, may be the dominant excitation mechanism for producing CO•.(v0 in the 85-km altitude region. The evidence is based on 4.3-•tm zenith radiance data obtained on April 11, 1974, via a rocket-borne liquid N•. nitrogen cooled circular variable-filtered radiometer. The rocket was launched at night from the Poker Flat Research Range, Alaska. The data were obtained under conditions of essentially zero auroral activity. There is a feature in these data near 85-km altitude which can be explained by the mechanism OH(v)• N•." • CO,(its) --} CO•. q-hp4.a,m. A column transfer of 0.12 ñ 0.025 erg/cm 2 s from OH • to N•. is required to explain the feature. An alternate explanation on the basis of just the mechanisms I and 2 which are cited above requires that there existed a very unlikely mesospheric temperature profile on the evening of April 11, 1974. A similar nonauroral feature appears consistently near 85 km in preliminary 4.3-um zenith data obtained on March 27, 1973, February 25, 1974, March 6, 1975, and March 12, 1975, thus providing further evidence that the feature most likely results from the OH mechanism rather than a strange mesopheric temperature profile. These data were obtained from the Air

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 fly-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 nonexpert users to perform certain tasks, such as image or video annotation. In this work, we use crowdsourcing for the examination of complete VC fly-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 workflow, we achieve an overall polypsper-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.

We present crowdsourcing as an additional modality to aid radiologists in the diagnosis of lung cancer from clinical chest computed tomography (CT) scans. More specifically, a complete workflow is introduced which can help maximize the sensitivity of lung nodule detection by utilizing the collective intelligence of the crowd. We combine the concept of overlapping thin-slab maximum intensity projections (TS-MIPs) and cine viewing to render short videos that can be outsourced as an annotation task to the crowd. These videos are generated by linearly interpolating overlapping TS-MIPs of CT slices through the depth of each quadrant of a patient's lung. The resultant videos are outsourced to an online community of non-expert users who, after a brief tutorial, annotate suspected nodules in these video segments. Using our crowdsourcing workflow, we achieved a lung nodule detection sensitivity of over 90% for 20 patient CT datasets (containing 178 lung nodules with sizes between 1-30mm), and only 47 false positives from a total of 1021 annotations on nodules of all sizes (96% sensitivity for nodules>4mm). These results show that crowdsourcing can be a robust and scalable modality to aid radiologists in screening for lung cancer, directly or in combination with computer-aided detection (CAD) algorithms. For CAD algorithms, the presented workflow can provide highly accurate training data to overcome the high false-positive rate (per scan) problem. We also provide, for the first time, analysis on nodule size and position which can help improve CAD algorithms.

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