Bone enhancement in ultrasound using local spectrum variations for guiding percutaneous scaphoid fracture fixation procedures
Abstract
Purpose The scaphoid bone is the most frequently fractured bone in the wrist. When fracture fixation is indicated, a screw is inserted into the bone either in an open surgical procedure or percutaneously under fluoroscopic guidance. Due to the complex geometry of the wrist, fracture fixation is a challenging task. Fluoroscopic guidance exposes both the patient and the physician to ionizing radiation. Ultrasound-based guidance has been suggested as a real-time, radiation-free alternative. The main challenge of using ultrasound is the difficulty in interpreting the images due to the low contrast and noisy nature of the data.Methods We propose a bone enhancement method that exploits local spectrum features of the ultrasound image. These features are utilized to design a set of quadrature band-pass filters and subsequently estimate the local phase symmetry, where high symmetry is expected at the bone locations. We incorporate the shadow information below the bone surfaces to further enhance the bone responses. The extracted bone surfaces are then used to register a statistical wrist model to ultrasound volumes, allowing the localization and interpretation of the scaphoid bone in the volumes.Results Feasibility experiments were performed using phantom and in vivo data. For phantoms, we obtain a surface distance error 1.08 mm and an angular deviation from the main axis of the scaphoid bone smaller than 5°, which are better compared to previously presented approaches.Conclusion The results are promising for further development of a surgical guidance system to enable accurate anatomy localization for guiding percutaneous scaphoid fracture fixations.
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Recent applications of deep learning in medical US analysis have involved classification, segmentation, detection, registration, biometric measurements, quality assessment, image-guided interventions, and therapy.
“Recent applications of deep learning in medical US analysis have involved various tasks, such as traditional diagnosis tasks including classification, segmentation, detection, registration, biometric measurements, and quality assessment, as well as emerging tasks including image-guided interventions and therapy [16] (Fig. 1).”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [16]
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Deep Learning in Medical Ultrasound Analysis: A Review
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- Authors
- Anas, E. M. A., Seitel, A., Rasoulian, A., John, P. S., Pichora, D., Darras, K., Wilson, D., Lessoway, V. A., Hacihaliloglu, I., Mousavi, P., Rohling, R., & Abolmaesumi, P.
- Affiliations
- University of British Columbia (CA)
- Kingston General Hospital (CA)
- Vancouver General Hospital (CA)
- B.C. Women's Hospital & Health Centre (CA)
- Rutgers, The State University of New Jersey (US)
- Queen's University (CA)
- Corresponding author
- Anas, Emran Mohammad Abu
- ORCID
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2015-04-07
- Volume
- 10
- Issue
- 6
- Pages
- 959-969
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- OpenAlex
- W2061789203
- Cited by
- 27
- References
- 33
- Citations by year
- 2026: 5 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 4 · 2017: 5 · 2016: 2
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- scaphoid fracture, percutaneous fracture fixation, phase symmetry, bandpass filter, shadow information
- MeSH terms
- Bone Screws, Fracture Fixation, Internal, Humans, Ultrasonography, Interventional, Scaphoid Bone, Fractures, Bone
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