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Published VersionCC BY V4.0, Open Access
Journal article
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
Scientific Reports, Vol.11, pp.1-12
2021
PMID: 34211081
Abstract
This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.
Details
- Title
- A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
- Authors
- Jared Hamwood (Author) - Queensland University of TechnologyBeat Schmutz (Author) - Queensland University of TechnologyMichael J. Collins (Author) - Queensland University of TechnologyMark C. Allenby (Author) - Queensland University of TechnologyDavid Alonso-Caneiro (Corresponding Author) - Queensland University of Technology
- Publication details
- Scientific Reports, Vol.11, pp.1-12
- Publisher
- Nature Publishing Group
- Date published
- 2021
- DOI
- 10.1038/s41598-021-93227-3
- ISSN
- 2045-2322
- PMID
- 34211081
- Copyright note
- © The Author(s) 2021 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons. org/ licenses/ by/4. 0/.
- Data Availability
- The datasets analysed during the current study are currently not publicly available. However, the algorithms developed in this work are available from the corresponding author on reasonable request.
- Grant note
- DAC was supported by a Rebecca L. Cooper 2018 Project Grant and the National Health & Medical Research Council Ideas Grant (APP1186915). MCA is supported by an Advance Queensland Fellowship (AQIRF1312018). MCA and DAC are supported by an IHBI Inter-Theme Collaboration Grant.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99973598802621
- Output Type
- Journal article
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- Radiology, Nuclear Medicine & Medical Imaging
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