Purpose 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. Methods 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. Results The developed machine learning framework was capable of segmenting the screw shafts with 93% and
International Journal of Computer Assisted Radiology and Surgery – Springer Journals
Published: May 28, 2018
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