Attention-guided multi-scale CNN network for cervical vertebral maturation assessment from lateral cephalometric radiography

摘要: Accurate determination of skeletal maturation indicators is crucial in the orthodontic process. Chronologic age is not a reliable skeletal maturation indicator, thus physicians use bone age. In orthodontics, the treatment timing depends on Cervical Vertebral Maturation (CVM) assessment. Determination of CVM degree remains challenging due to the limited annotated dataset, the existence of significant irrelevant areas in the image, the huge intra-class variances, and the high degree of inter-class similarities. To address this problem, researchers have started looking for external information beyond current available medical datasets. This work utilizes the domain knowledge from radiologists to train neural network models that can be utilized as a decision support system. We proposed a novel supervised learning method with a multi-scale attention mechanism, and we incorporated the general diagnostic patterns of medical doctors to classify lateral X-ray images as six CVM classes. The proposed network highlights the important regions, surpasses the irrelevant part of the image, and efficiently models long-range dependencies. Employing the attention mechanism improves both the performance and interpretability. In this work, we used additive spatial and channel attention modules. Our proposed network consists of three branches. The first branch extracts local features, and creates attention maps and related masks, the second branch uses the masks to extract discriminative features for classification, and the third branch fuses local and global features. The result shows that the proposed method can represent more discriminative features …