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Article

Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery

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Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
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Philips Healthcare, 5684 PC Best, The Netherlands
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Philips Research, 5656 AE, HTC 34 Eindhoven, The Netherlands
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Department of Neurosurgery, Karolinska University Hospital and Department of Clinical Neuroscience, Karolinska Institutet, SE-171 46 Stockholm, Sweden
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Quest Medical Imaging BV, 1775 PW Middenmeer, The Netherlands
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4078; https://doi.org/10.3390/app10124078
Received: 13 May 2020 / Revised: 28 May 2020 / Accepted: 9 June 2020 / Published: 12 June 2020
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications)
In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers. View Full-Text
Keywords: hyperspectral imaging; feature detection; spine surgery; markerless tracking; deep local features hyperspectral imaging; feature detection; spine surgery; markerless tracking; deep local features
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MDPI and ACS Style

Manni, F.; van der Sommen, F.; Zinger, S.; Shan, C.; Holthuizen, R.; Lai, M.; Buström, G.; Hoveling, R.J.M.; Edström, E.; Elmi-Terander, A.; de With, P.H.N. Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery. Appl. Sci. 2020, 10, 4078. https://doi.org/10.3390/app10124078

AMA Style

Manni F, van der Sommen F, Zinger S, Shan C, Holthuizen R, Lai M, Buström G, Hoveling RJM, Edström E, Elmi-Terander A, de With PHN. Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery. Applied Sciences. 2020; 10(12):4078. https://doi.org/10.3390/app10124078

Chicago/Turabian Style

Manni, Francesca, Fons van der Sommen, Svitlana Zinger, Caifeng Shan, Ronald Holthuizen, Marco Lai, Gustav Buström, Richelle J.M. Hoveling, Erik Edström, Adrian Elmi-Terander, and Peter H.N. de With. 2020. "Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery" Applied Sciences 10, no. 12: 4078. https://doi.org/10.3390/app10124078

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