Detection of degenerative osteophytes of the spine on PET/CT using region-based convolutional neural networks

Abstract

The identification and detection of degenerative osteophytes of the spine is a challenging and time-consuming task that is important for the diagnosis of many spine diseases. Previous attempts to automate this task have been focused on using image features derived from radiographic diagnostic expertise rather than directly learning features. In this paper, we present a bottom-up approach to generate features for classification using a region-based convolutional neural network with unwrapped cortical shell maps from 18F-NaF positron emission tomography and computed tomography scans of the vertebral bodies of the thoracic and lumbar spine. We evaluated osteophyte detection performance on 45 individuals with 5-fold cross validation and achieved state-of-the-art performance with 85% sensitivity at 2 false positive detections per patient.