Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network

This study proposed a novel network, a multi-scale attention-guided network (MAGNet), to diagnose vertebral fractures and three-column injuries with fracture visualization at a vertebra level. By imposing attention constraints through a disease attention map (DAM), a fusion of multi-scale spatial attention maps, the MAGNet can get task highly relevant features and localize fractures. A total of 989 vertebrae were studied here. After four-fold cross-validation, the area under the ROC curve (AUC) of our model for vertebral fracture dichotomized diagnosis and three-column injury diagnosis was 0.884  ± 0.015 and 0.920 ± 0.104, respectively. The overall performance of our model outperformed classical classification models, attention models, visual explanation methods, and attention-guided methods based on class activation mapping. Our work can promote the clinical application of deep l earning to diagnose vertebral fractures and provide a way to visualize and improve the diagnosis results with attention constraints.Graphical Abstract
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research