Name
Title | ||
|---|---|---|
Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies |
Abstract | ||
|---|---|---|
Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they characteristically have extremely slow rates of growth. This problem is further magnified by the small size of many of these lesions now being routinely detected following the introduction of multislice CT scanners capable of acquiring contiguous high resolution 1 to 1.25 mm sections throughout the thorax in a single breathhold period. Although segmentation of solid nodules can be used clinically to determine volume doubling times quantitatively, reliable methods for seamentation of pure ground glass nodules have yet to be introduced. Our purpose is to evaluate a newly developed computer-based segmentation method for rapid and reproducible measurements of pure ground glass nodules. 23 pure or mixed around glass nodules were identified in a total of 8 patients by a radiologist and subsequently segmented by our computer-based method using Markov random field and shape analysis. The computer-based segmentation was initialized by a click point. Methodological consistency was assessed using the overlap ratio between 3 segmentations initialized by 3 different click points for each nodule. The 95% confidence interval on the mean of the overlap ratios proved to be [0.984, 0.998]. The computer-based method failed on two nodules that were difficult to segment even manually either due to especially low contrast or markedly irregular margins. While achieving consistent manual segmentation of around glass nodules has proven problematic most often due to indistinct boundaries and interobserver variability, our proposed method introduces a powerful new tool for obtaining reproducible quantitative measurements of these lesions. It is our intention to further document the value of this approach with a still larger set of ground glass nodules. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1117/12.536259 | Proceedings of SPIE |
Keywords | Field | DocType |
CAD,ground glass nodule (GGN),subsolid nodule,nodule segmentation,Markov random field (MRF) | Computer vision,Ct scanners,Pattern recognition,Segmentation,Markov random field,Computer science,Multislice,Artificial intelligence,Computing systems,Shape analysis (digital geometry) | Conference |
Volume | ISSN | Citations |
5370 | 0277-786X | 7 |
PageRank | References | Authors |
0.74 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 23 | 5.03 |
| ming fang | 2 | 13 | 3.84 |
| David P. Naidich | 3 | 98 | 12.61 |
| Carol L. Novak | 4 | 146 | 23.88 |