Automatic localisation of vertebrae in DXA images using random forest regression voting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a method for automatic detection and localisation of vertebrae in clinical images that was designed to avoid making a priori assumptions of how many vertebrae are visible. Multiple random forest regressors were trained to identify vertebral end-plates, providing estimates of both the location and pose of the vertebrae. The highest-weighted responses from each model were combined using a Hough-style voting array. A graphical approach was then used to extract contiguous sets of detections representing neighbouring vertebrae, by finding a path linking modes of high weight, subject to pose constraints. The method was evaluated on 320 lateral dual-energy X-ray absorptiometry spinal images with a high prevalence of osteoporotic vertebral fractures, and detected 92% of the vertebrae between T7 and L4 with a mean localisation error of 2.36 mm. When used to initialise a constrained local model segmentation of the vertebrae, the method increased the incidence of fit failures from 1.5 to 2.1% compared to manual initialisation, and produced no difference in fracture classification using a simple classifier.

Bibliographical metadata

Original languageEnglish
Title of host publicationComputational Methods and Clinical Applications for Spine Imaging - 3rd International Workshop and Challenge, CSI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
PublisherSpringer Nature
Pages38-51
Number of pages14
Volume9402
ISBN (Print)9783319418261
DOIs
Publication statusPublished - 2016
Event3rd International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2015 held in conjunction with 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Event duration: 5 Oct 20155 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9402
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2015 held in conjunction with 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period5/10/155/10/15