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MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images.

Kuang, X; Cheung, JP; Wu, H; Dokos, S; Zhang, T; (2020) MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). (pp. pp. 1633-1636). IEEE: Montreal, QC, Canada. Green open access

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Abstract

Most deep learning based vertebral segmentation methods require laborious manual labelling tasks. We aim to establish an unsupervised deep learning pipeline for vertebral segmentation of MR images. We integrate the sub-optimal segmentation results produced by a rule-based method with a unique voting mechanism to provide supervision in the training process for the deep learning model. Preliminary validation shows a high segmentation accuracy achieved by our method without relying on any manual labelling.The clinical relevance of this study is that it provides an efficient vertebral segmentation method with high accuracy. Potential applications are in automated pathology detection and vertebral 3D reconstructions for biomechanical simulations and 3D printing, facilitating clinical decision making, surgical planning and tissue engineering.

Type: Proceedings paper
Title: MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images.
Event: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/EMBC44109.2020.9175987
Publisher version: https://doi.org/10.1109/EMBC44109.2020.9175987
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep Learning, Magnetic Resonance Imaging, Printing, Three-Dimensional, Spine
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10131711
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