Comparing Image Segmentation Techniques for Determining 3D Orbital Cavernous Hemangioma Size on MRI
| Autor: | Michelle M Maeng, Michael Kazim, Mary Dahl Maher, Andrea A. Tooley, Kristen E. Dunbar, Ranjodh S Boparai, Kyle J. Godfrey |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Concordance GrowCut algorithm Ellipsoid method General Medicine Image segmentation Magnetic Resonance Imaging Standard deviation 03 medical and health sciences Ophthalmology 0302 clinical medicine Concordance correlation coefficient Hemangioma Cavernous 030221 ophthalmology & optometry Medicine Humans Orbital Neoplasms Surgery Segmentation business Cluster analysis Nuclear medicine |
| Zdroj: | Ophthalmic plastic and reconstructive surgery. 36(6) |
| ISSN: | 1537-2677 |
| Popis: | Purpose To measure orbital cavernous hemangioma size using 3 segmentation methods requiring different degrees of subjective judgment, and to evaluate interobserver agreement using these methods. Methods Fourteen patients with orbital cavernous hemangiomas were included in the study. Pretreatment T2-weighted MRIs were analyzed by 2 observers using 3 methods, including 1 user-dependent image segmentation method that required high degrees of subjective judgment (ellipsoid) and 2 parameter-dependent methods that required low degree of subjective judgment (GrowCut and k-means clustering segmentation). Interobserver agreement was assessed using Lin's concordance correlation coefficients. Results Using the ellipsoid method, the average tumor sizes calculated by the 2 observers were 1.68 ml (standard deviation [SD] 1.45 ml) and 1.48 ml (SD 1.19 ml). Using the GrowCut method, the average tumor sizes calculated by the 2 observers were 3.00 ml (SD 2.46 ml) and 6.34 ml (SD 3.78 ml). Using k-means clustering segmentation, the average tumor sizes calculated by the 2 observers were 2.31 ml (SD 1.83 ml) and 2.12 ml (SD 1.87 ml). The concordance correlation coefficient for the ellipsoid, GrowCut, and k-means clustering methods were 0.92 (95% CI, 0.83-0.99), 0.12 (95% CI, -0.21 to 0.44), and 0.95 (95% CI, 0.90-0.99), respectively. Conclusions k-means clustering, a parameter-dependent method with low degree of subjective judgment, showed better interobserver agreement compared with the ellipsoid and GrowCut methods. k-means clustering clearly delineated tumor boundaries and outlined components of the tumor with different signal intensities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|