Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Lu, Ping; Barazzetti, Livia; Chandran, Vimal; Gerber, Kate; Weber, Stefan; Gerber, Nicolas; Reyes, Mauricio (August 2015). Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique. IEEE Engineering in Medicine and Biology Society conference proceedings, 2015, pp. 2964-2967. IEEE Service Center 10.1109/EMBC.2015.7319014

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Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy > ARTORG Center - Artificial Hearing Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy

UniBE Contributor:

Lu, Ping; Barazzetti, Livia; Chandran, Vimal; Gerber, Kate; Weber, Stefan; Gerber, Nicolas and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1557-170X

ISBN:

978-1-4244-9271-8

Publisher:

IEEE Service Center

Language:

English

Submitter:

Lars Marius Schwalbe

Date Deposited:

04 Sep 2015 15:50

Last Modified:

15 Jan 2017 02:01

Publisher DOI:

10.1109/EMBC.2015.7319014

PubMed ID:

26736914

BORIS DOI:

10.7892/boris.71456

URI:

https://boris.unibe.ch/id/eprint/71456

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