Abstract: Osteoporotic Vertebral Fracture (OVFs) is a common lumbar spine disorder that severely affects the health of patients. With a clear bone blocks boundary, CT images have gained obvious advantages in OVFs diagnosis. Compared with CT images, X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping shadows. Considering how to transfer CT imaging advantages to achieve OVFs classification in X-rays is meaningful. For this purpose, we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency features. Different from existing methods, we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and X-ray. In the meanwhile, the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant features. We employ five evaluation metrics to compare the proposed method with the state-of-the-art methods. The final results show that our method improves the best value of AUC from 86.32 to 92.16%. The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

Key words: Osteoporotic vertebral fracture classification , · Cross-modality , · Unsupervised domain adaptation , · Transfer learning , · Convolutional neural network , · Computer-aided diagnosis