ALiGN: Attention based Line Guided Network for Vertebral Comprssion Fracture Detection

Vertebral Compression Fracture (VCF) is one of the common fractures, especially for elderlies. As it affects postural deformation that may cause secondary disorders in the respiratory or digestive system if not treated in time, diagnosis of VCF is crucial. Using deep learning model based detection technology in diagnosis can reduce the workload of healthcare workers and misdiagnosis. Hence in this work, we propose ALiGN, a compression fracture detection model in the lumbar vertebra based on a deep convolutional neural network (CNN). Specifically, we take the location of each vertebral body into account via a feature pyramid network with an attention mechanism. Our proposed model outperforms the earlier works with a sensitivity 0.9729, specificity 0.9914, and mAP 0.7882.