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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 02805naa a2200373 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:144255673 | |
| 003 | SwePub | |
| 008 | 240916s2020 | |||||||||||000 ||eng| | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:1442556732 URI |
| 024 | 7 | a https://doi.org/10.3390/s201336412 DOI |
| 040 | a (SwePub)ki | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Manni, F4 aut |
| 245 | 1 0 | a Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery |
| 264 | c 2020-06-29 | |
| 264 | 1 | b MDPI AG,c 2020 |
| 520 | a Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < 0.5 mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery. | |
| 700 | 1 | a Elmi-Terander, Au Karolinska Institutet4 aut |
| 700 | 1 | a Burstrom, Gu Karolinska Institutet4 aut |
| 700 | 1 | a Persson, Ou Karolinska Institutet4 aut |
| 700 | 1 | a Edstrom, Eu Karolinska Institutet4 aut |
| 700 | 1 | a Holthuizen, R4 aut |
| 700 | 1 | a Shan, C4 aut |
| 700 | 1 | a Zinger, S4 aut |
| 700 | 1 | a van der Sommen, F4 aut |
| 700 | 1 | a de With, PHN4 aut |
| 710 | 2 | a Karolinska Institutet4 org |
| 773 | 0 | t Sensors (Basel, Switzerland)d : MDPI AGg 20:13q 20:13x 1424-8220 |
| 856 | 4 | u https://www.mdpi.com/1424-8220/20/13/3641/pdf |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:144255673 |
| 856 | 4 8 | u https://doi.org/10.3390/s20133641 |
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