Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

Details

Serval ID
serval:BIB_B68F51ECA6F0
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.
Journal
International Journal of Radiation Oncology, Biology, Physics
Author(s)
Rüegsegger M.B., Bach Cuadra M., Pica A., Amstutz C.A., Rudolph T., Aebersold D., Kowal J.H.
ISSN
1879-355X (Electronic)
ISSN-L
0360-3016
Publication state
Published
Issued date
2012
Peer-reviewed
Oui
Volume
84
Number
4
Pages
e541-e547
Language
english
Notes
Publication types: Journal Article
Abstract
PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors.
METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert.
RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer.
CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.
Pubmed
Web of science
Create date
06/12/2012 19:35
Last modification date
20/08/2019 16:24
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