Semiautomatic extraction of cortical thickness and diaphyseal curvature from CT scans.
Ján DupejAlizé Lacoste JeansonJosef PelikánJaroslav BrůžekPublished in: American journal of physical anthropology (2017)
The understanding of locomotor patterns, activity schemes, and biological variations has been enhanced by the study of the geometrical properties and cortical bone thickness of the long bones measured using CT scan cross-sections. With the development of scanning procedures, the internal architecture of the long bones can be explored along the entire diaphysis. Recently, several methods that map cortical thickness along the whole femoral diaphysis have been developed. Precise homology is vital for statistical examination of the data; however, the repeatability of these methods is unknown and some do not account for the curvature of the bones. We have designed a semiautomatic workflow that improves the morphometric analysis of cortical thickness, including robust data acquisition with minimal user interaction and considering the bone curvature. The proposed algorithm also performs automatic landmark refinement and rigid registration on the extracted morphometric maps of the cortical thickness. Because our algorithm automatically reslices the diaphysis into 100 cross-sections along the medial axis and uses an adaptive thresholding method, it is usable on CT scans that contain soft tissues as well as on bones that have not been oriented specifically prior to scanning. Our approach exhibits considerable robustness to error in user-supplied landmarks, suppresses distortion caused by the curvature of the bones, and calculates the curvature of the medial axis.
Keyphrases
- computed tomography
- dual energy
- optical coherence tomography
- contrast enhanced
- image quality
- machine learning
- deep learning
- electronic health record
- positron emission tomography
- bone mineral density
- spinal cord injury
- big data
- magnetic resonance
- signaling pathway
- bone loss
- postmenopausal women
- body composition
- bone regeneration