Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Fully Automatic Localisation of Vertebrae in CT Images Using Random Forest Regression Voting
verfasst von : Paul A. Bromiley, Eleni P. Kariki, Judith E. Adams, Timothy F. Cootes
Erschienen in: Computational Methods and Clinical Applications for Spine Imaging
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
We describe a system for fully automatic vertebra localisation and segmentation in 3D CT volumes containing arbitrary regions of the spine, with the aim of detecting osteoporotic fractures. To avoid the difficulties of high-resolution manual annotation on overlapping structures in 3D, the system consists of several 2D operations. First, a Random Forest regressor is used to localise the spinal midplane in a coronal maximum intensity projection. A 2D sagittal image showing the midplane is then produced. A second set of regressors are used to localise each vertebral body in this image. Finally, a Random Forest Regression Voting Constrained Local Model is used to segment each detected vertebra.
The system was evaluated on 402 CT volumes. 83% of vertebrae between T4 and L4 were detected and, of these, 97% were segmented with a mean error of less than or equal to \(1\,mm\). A simple classifier was applied to perform a fracture/non-fracture classification for each image, achieving 69% recall at 70% precision.