Computer-assisted contralateral side comparison of the ankle joint using flat panel technology

Autor: Jan Siad El Barbari, Celia Martín Vicario, Lisa Kausch, Maxim Privalov, Sarina Thomas, Klaus H. Maier-Hein, Jochen Franke, André Klein, Holger Kunze
Rok vydání: 2021
Předmět:
Reoperation
Plane estimation
Computer-assisted surgery
Computer science
medicine.medical_treatment
Biomedical Engineering
Ankle bone
Health Informatics
02 engineering and technology
Ankle Fractures
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
Fracture Fixation
Internal
Intraoperative Period
0302 clinical medicine
Imaging
Three-Dimensional
Segmentation
Sliding window protocol
0202 electrical engineering
electronic engineering
information engineering
medicine
Image Processing
Computer-Assisted
medicine.bone
Humans
Radiology
Nuclear Medicine and imaging
Computer vision
Osteosynthesis
business.industry
Visual comparison
Reproducibility of Results
General Medicine
Computer Graphics and Computer-Aided Design
Computer Science Applications
medicine.anatomical_structure
020201 artificial intelligence & image processing
Surgery
Original Article
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks
Computer
Ankle
business
Algorithms
Ankle Joint
Zdroj: International Journal of Computer Assisted Radiology and Surgery
ISSN: 1861-6429
Popis: Purpose Reduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation. Method In this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment. Results Experiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of $$0.73\pm 1.36$$ 0.73 ± 1.36 mm and a median angular error between 2.98$$^\circ $$ ∘ and 3.71$$^\circ $$ ∘ for the plane normals. Conclusion Standard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery.
Databáze: OpenAIRE
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