Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data
Purpose Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT). Materials and methods This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively col...
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Veröffentlicht in: | Radiologia medica 2020-01, Vol.125 (1), p.48-56 |
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Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Purpose
Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
Materials and methods
This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
Results
The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
Conclusion
The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans. |
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ISSN: | 0033-8362 1826-6983 |
DOI: | 10.1007/s11547-019-01079-9 |