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Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

ABSTRACT: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography.This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification.The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041).The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.

SUBMITTER: Nguyen TB

PROVIDER: S-EPMC3285221 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

Nguyen Tan B TB Wang Shijun S Anugu Vishal V Rose Natalie N McKenna Matthew M Petrick Nicholas N Burns Joseph E JE Summers Ronald M RM

Radiology 20120124 3

<h4>Purpose</h4>To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography.<h4>Materials and methods</h4>This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed ...[more]

PMID: 22274839

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Project description:Purpose To report the prevalence and characteristics of serrated polyps identified in a large, average-risk population undergoing screening computed tomographic (CT) colonography. Materials and Methods This HIPAA-compliant retrospective study was approved by the institutional review board of the University of Wisconsin School of Medicine and Public Health. The need for informed consent was waived. Nine thousand six hundred examinations from 8289 patients were enrolled in a single-institution CT colonography-based screening program (from 2004 to 2011) and were evaluated for the presence of nondiminutive serrated lesions and advanced adenomas. The prevalence and characteristics of these lesions were tabulated. Generalized estimating equation regressions of polyp characteristics that may contribute to visualization of serrated lesions were investigated, including polyp size, location, and morphologic appearance; histologic findings; and presence or absence of contrast material tagging. Results Nondiminutive serrated lesions (?6 mm) were seen at CT colonography-based screening with a prevalence of 3.1% (254 of 8289 patients). Sessile serrated adenomas (SSAs) and traditional serrated adenomas (TSAs) constituted 36.8% (137 of 372) and 4.3% (16 of 372) of serrated lesions, respectively; hyperplastic polyps (HPs) accounted for 58.9% (219 of 372 lesions). SSA and TSA tended to be large (mean size, 10.6 mm and 14.1 mm, respectively), with size categories and polyp subgroups significantly associated (P < .0001). SSA tended to be proximal in location (91.2%, 125 of 137 lesions) and flat in morphologic appearance (39.4%, 54 of 137 lesions) compared with TSA and HP. The presence of high-grade dysplasia in serrated lesions was uncommon when compared with advanced adenomas (one of 372 lesions vs 22 of 395 lesions, respectively; P < .0001). Multivariate analysis showed that contrast material tagging markedly improved serrated polyp detection with an odds ratio of 40.4 (95% confidence interval: 10.1, 161.4). Conclusion Serrated lesions are seen at CT colonography-based screening with a nondiminutive prevalence of 3.1%. These lesions tend to be large, flat, and proximal in location. Adherent contrast material coating on these polyps aids in their detection, despite an often flat morphologic appearance. (©) RSNA, 2016 Online supplemental material is available for this article.

Project description:To present a computer-aided detection tool for identifying, quantifying, and evaluating tuberculosis (TB) cavities in the infected lungs from computed tomography (CT) scans.The authors' proposed method is based on a novel shape-based automated detection algorithm on CT scans followed by a fuzzy connectedness (FC) delineation procedure. In order to assess interaction between cavities and airways, the authors first roughly identified air-filled structures (airway, cavities, esophagus, etc.) by thresholding over Hounsfield unit of CT image. Then, airway and cavity structure detection was conducted within the support vector machine classification algorithm. Once airway and cavities were detected automatically, the authors extracted airway tree using a hybrid multiscale approach based on novel affinity relations within the FC framework and segmented cavities using intensity-based FC algorithm. At final step, the authors refined airway structures within the local regions of FC with finer control. Cavity segmentation results were compared to the reference truths provided by expert radiologists and cavity formation was tracked longitudinally from serial CT scans through shape and volume information automatically determined through the authors' proposed system. Morphological evolution of the cavitary TB were analyzed accordingly with this process. Finally, the authors computed the minimum distance between cavity surface and nearby airway structures by using the linear time distance transform algorithm to explore potential role of airways in cavity formation and morphological evolution.The proposed methodology was qualitatively and quantitatively evaluated on pulmonary CT images of rabbits experimentally infected with TB, and multiple markers such as cavity volume, cavity surface area, minimum distance from cavity surface to the nearest bronchial-tree, and longitudinal change of these markers (namely, morphological evolution of cavities) were determined precisely. While accuracy of the authors' cavity detection algorithm was 94.61%, airway detection part of the proposed methodology showed even higher performance by 99.8%. Dice similarity coefficients for cavitary segmentation experiments were found to be approximately 99.0% with respect to the reference truths provided by two expert radiologists (blinded to their evaluations). Moreover, the authors noted that volume derived from the authors' segmentation method was highly correlated with those provided by the expert radiologists (R(2) = 0.99757 and R(2) = 0.99496, p < 0.001, with respect to the observer 1 and observer 2) with an interobserver agreement of 98%. The authors quantitatively confirmed that cavity formation was positioned by the nearby bronchial-tree after exploring the respective spatial positions based on the minimum distance measurement. In terms of efficiency, the core algorithms take less than 2 min on a linux machine with 3.47 GHz CPU and 24 GB memory.The authors presented a fully automatic method for cavitary TB detection, quantification, and evaluation. The performance of every step of the algorithm was qualitatively and quantitatively assessed. With the proposed method, airways and cavities were automatically detected and subsequently delineated in high accuracy with heightened efficiency. Furthermore, not only morphological information of cavities were obtained through the authors' proposed framework, but their spatial relation to airways, and longitudinal analysis was also provided to get further insight on cavity formation in tuberculosis disease. To the authors' best of knowledge, this is the first study in computerized analysis of cavitary tuberculosis from CT scans.

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Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

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Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

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