ArticleInternational journal of computer assisted radiology and surgery2014
Intraoperative detection and localization of cylindrical implants in cone-beam CT image data.
Article in International journal of computer assisted radiology and surgery, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Automatic multi-catheter detection using deeply supervised convolutional neural network in MRI-guided HDR prostate brachytherapy.Medical physics · 2020Article
- Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.Physics in medicine and biology · 2019Article
- Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy.IEEE transactions on medical imaging · 2019Article
- Spinal pedicle screw planning using deformable atlas registration.Physics in medicine and biology · 2017Article
- Articular surface segmentation using active shape models for intraoperative implant assessment.International journal of computer assisted radiology and surgery · 2016Article
- Intra-operative assessment of fractured articular surfaces in cone beam CT image data.International journal of computer assisted radiology and surgery · 2016Article
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10 authors.
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Abstract
purposeOrthopedic fractures are often fixed using metal implants. The correct positioning of cylindrical implants such as surgical screws, rods and guide wires is highly important. Intraoperative 3D imaging is often used to ensure proper implant placement. However, 3D image interaction is time-consuming and requires experience. We developed an automatic method that simplifies and accelerates location assessment of cylindrical implants in 3D images.
methodsOur approach is composed of three major steps. At first, cylindrical characteristics are detected by analyzing image gradients in small image regions. Next, these characteristics are grouped in a cluster analysis. The clusters represent cylindrical implants and are used to initialize a cylinder-to-image registration. Finally, the two end points are optimized regarding image contrast along the cylinder axis.
resultsA total of 67 images containing 420 cylindrical implants were used for testing. Different anatomical regions (calcaneus, spine) and various image sources (two mobile devices, three reconstruction methods) were investigated. Depending on the evaluation set, the detection performance was between 91.7 and 96.1% true- positive rate with a false-positive rate between 2.0 and 3.2%. The end point distance errors ranged from [Formula: see text] to [Formula: see text] mm and the orientation errors from [Formula: see text] to [Formula: see text] degrees. The average computation time was less than 5 seconds.
conclusionsAn automatic method was developed and tested that obviates the need for 3D image interaction during intraoperative assessment of cylindrical orthopedic implants. The required time for working with the viewing software of cone-beam CT device is drastically reduced and leads to a shorter time under anesthesia for the patient.
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