Name
Abstract | ||
|---|---|---|
Colonoscopy is currently the preferred screening modality for prevention of colorectal cancer. However, the effectiveness of colonoscopy depends on the quality of the procedure, which depends on several factors. In this paper, we present new methods that derive a new quality metric for automated scoring of quality of mucosa inspection performed by the endoscopist. We conducted Pearson's Correlation analysis of the computerized metric scores against the averages of the manual scores given by four domain experts on twenty-one colonoscopy videos. Our metric shows a relatively strong positive correlation ( Pearson's correlation coefficient of 0.72) between the computer-generated score and the ground truth. Hence, the proposed work is very promising to be used for quality control/assurance in routine colonoscopy screening. (C) 2010 Published by Elsevier Ltd. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.procs.2010.04.105 | Procedia Computer Science |
Keywords | Field | DocType |
Quality of colonoscopy,image analysis,objective quality metrics | Data mining,Correlation coefficient,Colonoscopy,Computer science,Ground truth,Positive correlation,Artificial intelligence,Colorectal cancer,Machine learning,Correlation analysis | Journal |
Volume | Issue | ISSN |
1 | 1 | 1877-0509 |
Citations | PageRank | References |
2 | 0.39 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuemin Liu | 1 | 2 | 0.39 |
| Wallapak Tavanapong | 2 | 535 | 63.27 |
| Johnny Wong | 3 | 500 | 49.19 |
| JungHwan Oh | 4 | 520 | 44.87 |
| Piet C. De Groen | 5 | 372 | 29.89 |