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A New Algorithm for Cortical Bone Segmentation with Its Validation and Applications to In Vivo Imaging
Cheng Li1, Dakai Jin1, Trudy L Burns2
1Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242.
Proceedings of the ... International Conference on Image Analysis and Processing. International Conference on Image Analysis and Processing
|July 12, 2016
Summary
An automated algorithm accurately segments cortical bone in CT scans. This method revealed males have thicker bone cortices and higher porosity than females.
Area of Science:
- Orthopedics and Biomedical Engineering
- Medical Imaging Analysis
Background:
- Cortical bone is crucial for skeletal strength and fracture risk assessment.
- Accurate cortical bone segmentation is challenging in in vivo multi-row detector CT (MD-CT) imaging due to resolution limitations and partial volume effects.
Purpose of the Study:
- To develop and validate an automated algorithm for cortical bone segmentation in in vivo MD-CT imaging of the distal tibia.
Main Methods:
- The algorithm employs a modified fuzzy distance transform and connectivity analyses to leverage contextual and topological bone information.
- Validation involved a cadaveric study for accuracy and repeat scans for reliability.
- An in vivo study compared cortical bone characteristics between male and female volunteers.
Main Results:
- The algorithm achieved 95.1% volume of agreement with true segmentations in cadaveric studies.
- Intra-class correlation for repeat MD-CT scans was 98.2%, indicating high reliability.
- In vivo analysis showed males possess a 16.3% thicker cortex and 4.7% greater porosity compared to females on average.
Conclusions:
- The developed automated segmentation algorithm is accurate and reliable for in vivo MD-CT imaging of distal tibia.
- Significant sex-based differences in cortical bone thickness and porosity were identified, with implications for skeletal health assessment.
