Abstract Details

Title Postoperative Cervical Spine Radiograph Analysis: Generative Adversarial Networks Which Erase Spinal Instrumentation Improve the Performance of Landmark Detection Models

Topic General Neurology

Presentation(s) P10 - Poster Session 10 (11:45 AM-12:45 PM)

Poster/Presentation
Number 4-012

Objective

In this work, we evaluate a framework which identifies and digitally “erases” confounding spine instrumentation from postoperative images for long-term Neurology follow up.

Background Radiographic parameters following cervical spine surgery are presently assessed through manual methods or with the aid of computer-assisted landmark detection. Existing radiographic algorithms are sometimes hindered by vertebral radio-opaque fixation instrumentation, resulting in poor registration.

Design/Methods Landmarks of interest included vertebral end plates, intervertebral disc heights and Cobb angles, and spinous process A/P tips. Four model architectures were compared: 1) standard U-Net architecture, 2) standard registration-based model, and 3,4) both standard models prefaced with a masking U-Net and Generative Adversarial Network (GAN) that digitally erases spine instrumentation. The GAN was pre-trained on 11,694 cervical spine x-rays from the NHANES-II dataset, with digitally-added fixation hardware phantoms. These four models were evaluated on 132 annotated postoperative lateral cervical spine radiographs.

Results Inter-rater reliability showed a normalized Euclidean Distance (ED) 1.56±0.14mm for landmark locations. The U-Net and registration models were able to locate C2-C6 vertebral parameters of interest, with an ED of 2.11±0.24mm and 1.91±0.22mm, respectively. The prefixed U-Net and registration models demonstrated ED of 1.61±0.18mm and 1.11±0.24mm, respectively. Using the tolerance benchmark of 1.56mm, precision scores were determined for each model: 88% vs. 95% for the prefixed U-Net (p = 0.0132) and 79% vs. 88% for the prefixed registration model (p = 0.0092). Subgroup analysis revealed that the prefixed models fared better (89% vs. 96%, p = 0.0004) for spinous processes vs. vertebral endplates, and that accuracy of computed landmarks was better for C2-C4 vs. C4-C6 (p = 0.0045).

Conclusions We demonstrate that GANs trained on spinal imaging can aid Neurologists with postoperative cervical spine radioimaging follow-up, and that when used as prefixed models, they outperform state-of-the-art detection models with efficacy similar to gold standard human-detected landmarks.

Authors/Disclosures
Yunting Yu, MD PRESENTER	Miss Yu has nothing to disclose.
Sachin Govind	Mr. Govind has nothing to disclose.
Nader Dahdaleh (Northwestern University)	No disclosure on file

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