Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data

Abstract

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.