DocumentCode
3242629
Title
A fast and fully automated approach to segment optic nerves on MRI and its application to radiosurgery
Author
Dolz, J. ; Leroy, H.A. ; Reyns, N. ; Massoptier, L. ; Vermandel, M.
Author_Institution
AQUILAB, Loos-les-Lille, France
fYear
2015
fDate
16-19 April 2015
Firstpage
1102
Lastpage
1105
Abstract
Delineating critical structures of the brain is required for advanced radiotherapy technologies to determine whether the dose from the proposed treatment will impair the functionality of those structures. Employing an automatic segmentation computer module in the radiation oncology treatment planning process has the potential to significantly increase the efficiency, cost-effectiveness, and, ultimately, clinical outcome of patients undergoing radiation therapy. Atlas-based segmentation has shown to be a suitable tool for the segmentation of large structures such as the brainstem or the cerebellum. However, smaller structures such as the optic nerves are more difficult to segment. In this work, we present a novel approach to automatically segment the optic nerves, which is based on Support Vector Machines (SVM). Compared to state of the art methods, the presented method obtained a better performance in regards to accuracy, robustness and processing time, being a suitable trade-off between these three factors.
Keywords
biomedical MRI; brain; cancer; eye; image segmentation; medical image processing; neurophysiology; planning; radiation therapy; support vector machines; MRI; SVM; advanced radiotherapy technology; atlas-based segmentation; automatic segmentation computer module; brain structure functionality impairment; brainstem segmentation; cerebellum segmentation; clinical outcome; critical brain structure delineation; fast optic nerve segmentation; fully automated optic nerve segmentation; optic nerve segmentation accuracy; optic nerve segmentation robustness; processing time; proposed treatment dose; radiation oncology treatment planning; radiosurgery application; radiotherapy cost effectiveness; radiotherapy efficiency; support vector machine; Biomedical optical imaging; Brain; Image segmentation; Optical imaging; Support vector machines; Three-dimensional displays; MRI; machine learning; optic nerves segmentation; radiosurgery; support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
Conference_Location
New York, NY
Type
conf
DOI
10.1109/ISBI.2015.7164064
Filename
7164064
Link To Document