Occlusion-Robust Visual Markerless Bone Tracking for Computer-Assisted Orthopedic Surgery

Conventional computer-assisted orthopedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. To address this limitation, some previous studies have successfully adapted the existing deep lea...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-11
Main Authors: Hu, Xue, Nguyen, Anh, Baena, Ferdinando Rodriguez y
Format: Article
Language:English
Subjects:
Biomedical applications
Biomedical optical imaging
Bone surgery
Bones
Cameras
Data collection
Deep learning
Femur
image processing
Knee
Navigation systems
neural networks
Occlusion
Optical tracking
Orthopedics
Robustness
Segmentation
Surgery
Target tracking
Three-dimensional displays
vision-based instrumentation and measurement
Visualization
Workflow
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Summary:Conventional computer-assisted orthopedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. To address this limitation, some previous studies have successfully adapted the existing deep learning framework to automatically segment and register the exposed femur surface for knee surgery, but these fail under real-world occlusion, which often happens during a real surgical procedure. Furthermore, such methods are hardware-specific and not accurate enough for clinic acceptance. In this article, we propose a learning-based RGB-D markerless tracking method that is robust against occlusion. To avoid expensive surgical data collection, which is a well-known challenge for surgical task training, we generate synthetic RGB-D data with various occlusion scenarios. A new segmentation network that features dynamic region-of-interest prediction and 3-D geometric segmentation is designed, to learn the occlusion-related knowledge from simulated instances. Intensive experiments show that our proposed method achieves new state-of-the-art results in markerless bone tracking. Furthermore, our method generalizes well to new cameras and new target models, including a cadaver, without the need for network retraining. By using a high-quality RGB-D camera, our proposed visual tracking method can achieve an accuracy of 1°-2° and 2-4 mm on a phantom knee, which meets the clinical standard.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3134764