A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy
| Autor: | Carolyn Anglin, Hooman Esfandiari, Antony J. Hodgson, Robyn S. Newell, John Street |
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| Rok vydání: | 2018 |
| Předmět: |
musculoskeletal diseases
Reoperation C arm fluoroscopy Computer science Radiography Biomedical Engineering Health Informatics Convolutional neural network 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Pedicle Screws Humans Radiology Nuclear Medicine and imaging Segmentation Computer vision Pedicle screw Pose business.industry Deep learning General Medicine musculoskeletal system equipment and supplies Computer Graphics and Computer-Aided Design Computer Science Applications surgical procedures operative Spinal Fusion Surgery Computer-Assisted Fluoroscopy Automatic segmentation Surgery Computer Vision and Pattern Recognition Artificial intelligence business Tomography X-Ray Computed 030217 neurology & neurosurgery |
| Zdroj: | International journal of computer assisted radiology and surgery. 13(8) |
| ISSN: | 1861-6429 |
| Popis: | Pedicle screw fixation is a challenging procedure with a concerning rates of reoperation. After insertion of the screws is completed, the most common intraoperative verification approach is to acquire anterior–posterior and lateral radiographic images, based on which the surgeons try to visually assess the correctness of insertion. Given the limited accuracy of the existing verification techniques, we identified the need for an accurate and automated pedicle screw assessment system that can verify the screw insertion intraoperatively. For doing so, this paper offers a framework for automatic segmentation and pose estimation of pedicle screws based on deep learning principles. Segmentation of pedicle screw X-ray projections was performed by a convolutional neural network. The network could isolate the input X-rays into three classes: screw head, screw shaft and background. Once all the screw shafts were segmented, knowledge about the spatial configuration of the acquired biplanar X-rays was used to identify the correspondence between the projections. Pose estimation was then performed to estimate the 6 degree-of-freedom pose of each screw. The performance of the proposed pose estimation method was tested on a porcine specimen. The developed machine learning framework was capable of segmenting the screw shafts with 93% and 83% accuracy when tested on synthetic X-rays and on clinically realistic X-rays, respectively. The pose estimation accuracy of this method was shown to be $$1.93^{\circ } \pm 0.64^{\circ }$$ and $$1.92 \pm 0.55\,\hbox {mm}$$ on clinically realistic X-rays. The proposed system offers an accurate and fully automatic pedicle screw segmentation and pose assessment framework. Such a system can help to provide an intraoperative pedicle screw insertion assessment protocol with minimal interference with the existing surgical routines. |
| Databáze: | OpenAIRE |
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