Name
Abstract | ||
|---|---|---|
The thorough detection of nodules in high-resolution CT lung scans is an increasingly difficult, labor-intensive, and critical radiological task. Recent clinical research on early lung cancer CT presentation has demonstrated the significant clinical need to detect the more subtle subsolid nodules as well as the traditional solid nodule. We have developed a model-based computer-aided detection (CAD) algorithm designed to automatically detect both of these nodule presentation types through the use of precise mathematical models that capture scanner physics and anatomy and pathology domain knowledge. Our model-based CAD algorithm utilizes a Bayesian framework for determining the probability of multiple competing anatomical and pathological events throughout the lung. Using this model-based CAD algorithm on 50 low-dose CT lung cancer screening cases, we measured a 3.9% average improvement in radiologist sensitivity (93.8% to 97.7%) with 8.3 false positives per case for all nodules greater than or equal to5 mm in size. This model-based approach can be easily extended to support additional anatomy and pathology models as clinical understanding and scanning practices improve. (C) 2003 Published by Elsevier Science B.V. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0531-5131(03)00446-1 | CARS 2003: COMPUTER ASSISTED RADIOLOGY AND SURGERY, PROCEEDINGS |
Keywords | Field | DocType |
CAD, model-based detection, CT, lung cancer screening | CAD,Lung cancer,Lung scans,Lung cancer screening,Lung,Radiology,Medicine,False positive paradox | Conference |
Volume | ISSN | Citations |
1256 | 0531-5131 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert A Kaucic | 1 | 0 | 0.34 |
| Colin C McCulloch | 2 | 0 | 0.34 |
| Paulo R. S. Mendonça | 3 | 610 | 50.38 |
| Deborah J Walter | 4 | 0 | 0.34 |
| Ricardo S. Avila | 5 | 267 | 29.96 |
| J. L. Mundy | 6 | 1352 | 280.31 |