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Enhancing Bone Segmentation in Ultrasound Imaging Using Physics-Informed Deep Learning Models
Computer-Assisted Orthopedic Surgery (CAOS) has been demonstrated to improve surgical precision in various procedures, including spinal fusion surgery, arthroplasty, and bone deformity correction [1,2]. Ultrasound, as a radiation-free, cost-effective, and portable alternative to CT and X-ray imaging, has been employed for real-time visualization of both soft tissues and bones through the reflection of acoustic waves. Despite its advantages, ultrasound imaging has inherent limitations such as low signal-to-noise ratio, acoustic shadowing, and speckle noise, which pose challenges for interpretation by surgeons. In our project, we have collected a dataset comprising over 100k ultrasound images with precise bone annotations. These bone labels are categorized into two classes: high-intensity regions (high signal-to-noise ratio) and low-intensity regions (low signal-to-noise ratio), as shown in Figure 1. According to experiment results, surgeons’ performance for bone labeling for low-intensity regions declined significantly compared to the high-intensity regions.
[1] Pandey, Prashant U., et al. "Ultrasound bone segmentation: A scoping review of techniques and validation practices." Ultrasound in Medicine & Biology 46.4 (2020): 921-935.
[2] Hohlmann, Benjamin, Peter Broessner, and Klaus Radermacher. "Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery–a review and future challenges." Computer Assisted Surgery 29.1 (2024): 2276055.
Keywords: Computer Vision, Deep Learning, Medical Image Analysis
Detailed in the attached file
Detailed in the attached file
Develop a deep learning model that incorporates ultrasound physics to enhance bone segmentation, particularly in low-intensity bone regions.
Working packages:
- Literature review about ultrasound image analysis
- Implementation of baseline methods including traditional methods and DL-based methods
- Develop a novel deep learning model that integrates ultrasound physics to improve bone segmentation in ultrasound images, with a focus on low-intensity regions.
- Performance benchmarking
- (Optional) Full bone surface segmentation
Develop a deep learning model that incorporates ultrasound physics to enhance bone segmentation, particularly in low-intensity bone regions.
Working packages: - Literature review about ultrasound image analysis - Implementation of baseline methods including traditional methods and DL-based methods - Develop a novel deep learning model that integrates ultrasound physics to improve bone segmentation in ultrasound images, with a focus on low-intensity regions. - Performance benchmarking - (Optional) Full bone surface segmentation
Luohong Wu, luohong.wu@balgrist.ch
Jonas Hein, jonas.hein@inf.ethz.ch
Luohong Wu, luohong.wu@balgrist.ch Jonas Hein, jonas.hein@inf.ethz.ch
Thesis proposal - Enhancing Bone Segmentation in Ultrasound Imaging Using Physics-Informed Deep Learning Models.pdf
