Compression Fractures Detection on CT
This work addresses the clinical challenge of diagnosing osteoporosis-related fractures in medical imaging, representing an incremental improvement in automated detection tools.
The authors tackled the problem of underdiagnosed vertebral compression fractures by developing an automated detection method using CT scans, achieving a detection algorithm that combines segmentation, CNN classification, and RNN prediction.
The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.