A Novel Image Processing Algorithm for Determining the Optimal Base Point of the Screw for Spinal Surgery

Zoltan Tamas Kocsis, Janos Kovacs

Abstract


In the preparation phase for a spinal surgery doctors usually use CT and MRI images.  Using these images they estimate the size, direction, and placement depth of the screw used for the surgery.  As a result, surgical errors can occur. Therefore, an algorithm for reducing surgical errors is needed for supporting the doctors in finding the optimal location for the surgery. The method developed in our earlier work could determine the contour line and the highest point of the vertebra (called the base point) where the screw should initially be placed.  In this paper, using the Bresenham line drawing method, we will propose a more accurate algorithm for determining the optimal base point, the direction and the extent of movement of the screw for the spinal surgery

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References


Elsarrag, M., Soldozy, S., Patel, P., Norat, P., Sokolowski, J. D., Park, M. S., ... Kalani, M. Y. S. (2019). Enhanced recovery after spine surgery: a systematic review. Neurosurgical Focus, 46(4), E3. doi:10.3171/2019.1.focus18700

Gill, S., Abolmaesumi, P., Fichtinger, G., Boisvert, J., Pichora, D., Borshneck, D., & Mousavi, P. (2012). Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine. Medical Image Analysis, 16(3), 662–674. doi:10.1016/j.media.2010.07.008

Janoos, F., Mosaliganti, K., Xu, X., Machiraju, R., Huang, K., & Wong, S. T. C. (2009). Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging. Medical Image Analysis, 13(1), 167–179. doi:10.1016/j.media.2008.06.019

Z, T Kocsis , Application of computer-assisted image processing in spinal surgery in B Csiszar,F Bodog; E Mezo, B Zavodi, (editor) / 8th INTERDISCIPLINARY DOCTORALCONFERENCE 2019 - CONFERENCE BOOK Pecs, Magyarorszag : Pecsi Tudomanyegyetem Doktorandusz Onkormanyzat, (2019) pp. 137-150. , 13 p. (in Hungarian)¨

M Vania, D Mureja, D Lee Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling - arXiv preprint arXiv:1712.01640, 2017 [cited 2019-10-17]. https://arxiv.org/abs/1712.01640v1

Zhang, H., Shen, X., Dong, L., Miao, S., Ma, Q., & Wang, Y. (2016). X-Ray Image Processing Methods in Minimally Invasive Spine Surgery. 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). doi:10.1109/ihmsc.2016.243

Sivaganesan, A., Hirsch, B., Phillips, F. M., & McGirt, M. J. (2018). Spine Surgery in the Ambulatory Surgery Center Setting: Value-Based Advancement or Safety Liability? Neurosurgery, 83(2), 159–165. doi:10.1093/neuros/nyy057

Elsarrag, M., Soldozy, S., Patel, P., Norat, P., Sokolowski, J. D., Park, M. S., ... Kalani, M. Y. S. (2019). Enhanced recovery after spine surgery: a systematic review. Neurosurgical Focus, 46(4), E3. doi:10.3171/2019.1.focus18700

Anatomy of the spine [cited 2020-08-25] https://www.sci-infopages.com/anatomy-of-the-spine/

Isaac N. Bankman Handbook of medical imaging 2nd edition, Academic Press, Inc. USA '2008

Bourgeois AC, Faulkner AR, Pasciak AS, Bradley YC.The evolution of image-guided lumbosacral spine surgery. Ann Transl Med 2015;3:69. doi:10.3978/j.issn.2305-5839.2015.02.01

Open Pedicle Screw System [cited 2020-01-30]. https://nanovistechnology.com/portfolio/distributed-pedicle-screw-systems [20] {Orthopedic Implants Catalogue [cited 2019-10-17]. https://www.indianorthopaedic.com/products.html

Rutkowski, D. R., Sun, D., Anderson, P. A., & Rold´an-Alzate, A. (2018). A method to design and manufacture low-cost patient-specific templates for spinal surgery: evaluation of multiple additive manufacturing methods. Journal of 3D Printing in Medicine, 2 169–177. doi:10.2217/3dp-2018-0019

Lu´ıs A., Navarro-Ramirez R., Kirnaz S., Nakhla J., H¨artl R. (2019) Navigated Spinal Fusion. In: Phillips F., Lieberman I., Polly Jr. D.,Wang M. (eds) Minimally Invasive Spine Surgery. Springer, Cham. 355-374 doi: https://doi.org/10.1007/978-3-030-19007-1 31

M Vania, D Mureja, D Lee Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling - arXiv preprint arXiv:1712.01640, 2017 [cited 2019-10-17]. https://arxiv.org/abs/1712.01640v1

DICOM Coordinate System [cited 2020-08-25] http://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect C.8.8.25.6.html

EvilDicom: https://github.com/rexcardan/Evil-DICOM [Cited: 2019.06.10]

EmguCv: http://www.emgu.com [letöltés dátuma: 2019.06.10]

s mediclal [cited 2019-10-17].http://7smedical.com/product-trauma.html

Orthopedic Implants Catalogue [cited 2019-10-17]. https://www.indianorthopaedic.com/products.html

Comparions between DDA and Bresenham Line Drawing algorithm .[cited 2019-10-17]. https://www.geeksforgeeks.org/comparions-between-dda-andbresenham-line-drawing-algorithm/

J. E. Bresenham, ”Algorithm for computer control of a digital plotter,” IBM Systems Journal, vol. 4, no. 1, pp. 25-30, 1965. doi: 10.1147/sj.41.0025

Bresenham Line Drawing Algorithm [cited 2019-10-17]. https://iq.opengenus.org/bresenham-line-drawining-algorithm/

The Bresenham Line-Drawing Algorithm. [cited 2019-10-17]. https://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html


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