Name
Title | ||
|---|---|---|
Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach. |
Abstract | ||
|---|---|---|
Airway diseases (e.g., asthma, emphysema, and chronic bronchitis) are extremely common worldwide. Any morphological variations (abnormalities) of airways may physically change airflow and ultimately affect the ability of the lungs in gas exchange. In this study, we describe a novel algorithm aimed to automatically identify airway walls depicted on CT images. The underlying idea is to place a three-dimensional (3D) surface model within airway regions and thereafter allow this model to evolve (deform) under predefined external and internal forces automatically to the location where these forces reach a state of balance. By taking advantage of the geometric and the density characteristics of airway walls, the evolution procedure is performed in a distance gradient field and ultimately stops at regions with the highest contrast. The performance of this scheme was quantitatively evaluated from several perspectives. First, we assessed the accuracy of the developed scheme using a dedicated lung phantom in airway wall estimation and compared it with the traditional full-width at half maximum (FWHM) method. The phantom study shows that the developed scheme has an error ranging from 0.04mm to 0.36mm, which is much smaller than the FWHM method with an error ranging from 0.16mm to 0.84mm. Second, we compared the results obtained by the developed scheme with those manually delineated by an experienced (>30years) radiologist on clinical chest CT examinations, showing a mean difference of 0.084mm. In particular, the sensitivity of the scheme to different reconstruction kernels was evaluated on real chest CT examinations. For the ‘lung’, ‘bone’ and ‘standard’ kernels, the average airway wall thicknesses computed by the developed scheme were 1.302mm, 1.333mm and 1.339mm, respectively. Our preliminary experiments showed that the scheme had a reasonable accuracy in airway wall estimation. For a clinical chest CT examination, it took around 4min for this scheme to identify the inner and outer airway walls on a modern PC. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.media.2012.11.003 | Medical Image Analysis |
Keywords | Field | DocType |
Airway wall,Segmentation,Active surface,Gradient distance field | Biomedical engineering,Imaging phantom,Ranging,Artificial intelligence,Airway,Radiographic Image Enhancement,Computer vision,Mean difference,Active surface,Internal forces,Segmentation,Radiology,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 3 | 1361-8415 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suicheng Gu | 1 | 98 | 5.76 |
| Carl Fuhrman | 2 | 39 | 2.57 |
| Xin Meng | 3 | 2 | 0.71 |
| Jill M Siegfried | 4 | 9 | 1.28 |
| David Gur | 5 | 120 | 31.52 |
| Joseph K. Leader | 6 | 38 | 5.88 |
| Frank C Sciurba | 7 | 51 | 4.32 |
| Jiantao Pu | 8 | 277 | 23.12 |