Publication: Whole Spine Segmentation Using Object Detection and Semantic Segmentation
Whole Spine Segmentation Using Object Detection and Semantic Segmentation
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Da Mutten, R., Zanier, O., Theiler, S., Ryu, S.-J., Regli, L., Serra, C., & Staartjes, V. E. (2024). Whole Spine Segmentation Using Object Detection and Semantic Segmentation. Neurospine, 21(1), 57–67. https://doi.org/10.14245/ns.2347178.589
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OBJECTIVE
Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtu
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Creators (Authors)
- Da Mutten, Raffaele
- Zanier, Olivier
- Theiler, Sven
- Ryu, Seung-Jun
- Regli, Luca
- Serra, Carlo
- Staartjes, Victor E
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Da Mutten, R., Zanier, O., Theiler, S., Ryu, S.-J., Regli, L., Serra, C., & Staartjes, V. E. (2024). Whole Spine Segmentation Using Object Detection and Semantic Segmentation. Neurospine, 21(1), 57–67. https://doi.org/10.14245/ns.2347178.589
