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

Radiol Med. 2020 Jan;125(1):48-56. doi: 10.1007/s11547-019-01079-9. Epub 2019 Sep 14.


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.

Keywords: Artificial intelligence; Computed tomography; Decision forest; Spine.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Anatomic Landmarks / diagnostic imaging
  • Datasets as Topic
  • Decision Trees*
  • Humans
  • Machine Learning*
  • Middle Aged
  • Multidetector Computed Tomography / methods*
  • Retrospective Studies
  • Spine / diagnostic imaging*
  • Young Adult