A Machine Learning Framework for Intraoperative Segmentation and Quality Assessment of Pedicle Screw X-Rays

7 pages•Published: June 13, 2017

Hooman Esfandiari, Carolyn Anglin, John Street, Pierre Guy and Antony J. Hodgson

Abstract

Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two-dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws’ head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility.
In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%.
In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system.

Keyphrases: automation, c arm, machine learning, pedicle screw, quality assessment, segmentation

In: Klaus Radermacher and Ferdinando Rodriguez Y Baena (editors). CAOS 2017. 17th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 1, pages 144-150.

Links:	https://easychair.org/publications/paper/jKWn
	https://doi.org/10.29007/x9mr

BibTeX entry

@inproceedings{CAOS2017:Machine_Learning_Framework_Intraoperative,
  author    = {Hooman Esfandiari and Carolyn Anglin and John Street and Pierre Guy and Antony J. Hodgson},
  title     = {A Machine Learning Framework for Intraoperative Segmentation and Quality Assessment of Pedicle Screw X-Rays},
  booktitle = {CAOS 2017. 17th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Klaus Radermacher and Ferdinando Rodriguez Y Baena},
  series    = {EPiC Series in Health Sciences},
  volume    = {1},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/jKWn},
  doi       = {10.29007/x9mr},
  pages     = {144-150},
  year      = {2017}}

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