Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks

Abstract

Accurate localization and identification of vertebrae in 3D spinal images is essential for many clinical tasks. However, automatic localization and identification of vertebrae remains challenging due to similar appearance of vertebrae, abnormal pathological curvatures and image artifacts induced by surgical implants. Traditional methods relying on hand-crafted low level features and/or a priori knowledge usually fail to overcome these challenges on arbitrary CT scans. We present a robust and efficient approach to automatically locating and identifying vertebrae in 3D CT volumes by exploiting high level feature representations with deep convolutional neural network (CNN). A novel joint learning model with CNN (J-CNN) is proposed by considering both the appearance of vertebrae and the pairwise conditional dependency of neighboring vertebrae. The J-CNN can effectively identify the type of vertebra and eliminate false detections based on a set of coarse vertebral centroids generated from a random forest classifier. Furthermore, the predicted centroids are refined by a shape regression model. Our approach was quantitatively evaluated on the dataset of MICCAI 2014 Computational Challenge on Vertebrae Localization and Identification. Compared with the state-of-the-art method [1], our approach achieved a large margin with 10.12% improvement of the identification rate and smaller localization errors.