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Injuries of the spine, and its posterior elements in particular, are a commonoccurrence in trauma patients, with potentially devastating consequences.Computer-aided detection (CADe) could assist in the detection andclassification of spine fractures. Furthermore, CAD could help assess thestability and chronicity of fractures, as well as facilitate research intooptimization of treatment paradigms.  In this work, we apply deep convolutional networks (ConvNets) for theautomated detection of posterior element fractures of the spine. First, thevertebra bodies of the spine with its posterior elements are segmented in spineCT using multi-atlas label fusion. Then, edge maps of the posterior elementsare computed. These edge maps serve as candidate regions for predicting a setof probabilities for fractures along the image edges using ConvNets in a 2.5Dfashion (three orthogonal patches in axial, coronal and sagittal planes). Weexplore three different methods for training the ConvNet using 2.5D patchesalong the edge maps of 'positive', i.e. fractured posterior-elements and'negative', i.e. non-fractured elements.  An experienced radiologist retrospectively marked the location of 55displaced posterior-element fractures in 18 trauma patients. We randomly splitthe data into training and testing cases. In testing, we achieve anarea-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at5 or 10 false-positives per patient, respectively. Analysis of our set oftrauma patients demonstrates the feasibility of detecting posterior-elementfractures in spine CT images using computer vision techniques such as deepconvolutional networks.

Deep convolutional networks for automated detection of posterior-element fractures on spine CT