Fast Automatic Vertebrae Detection and Localization in Pathological CT Scans - A Deep Learning Approach

Automatic detection and localization of vertebrae in medical images are highly sought after techniques for computer-aided diagnosis systems of the spine. However, the presence of spine pathologies and surgical implants, and limited field-of-view of the spine anatomy in these images, make the development of these techniques challenging. This paper presents an automatic method for detection and localization of vertebrae in volumetric computed tomography (CT) scans. The method makes no assumptions about which section of the vertebral column is visible in the image. An efficient approach based on deep feed-forward neural networks is used to predict the location of each vertebra using its contextual information in the image. The method is evaluated on a public data set of 224 arbitrary-field-of-view CT scans of pathological cases and compared to two state-of-the-art methods. Our method can perform vertebrae detection at a rate of 96% with an overall run time of less than 3 seconds. Its fast and comparably accurate detection makes it appealing for clinical diagnosis and therapy applications.