Multi-scale Segmentation Network for Rib Fracture Classification from CT Images.

As the most common thoracic trauma, rib fracture classification is essential for clinical evaluation and treatment planning. However, it is challenging for manual identification and classification, due to the tiny size and blurriness of rib fracture in CT images. For automatic classification of rib fractures, conventional methods using hand-crafted features are low in robustness and generalizability. Though previous deep learning-based method shows improved the performance, they empirically normalized all fractures using one size, which ended up in alteration of fracture patterns. Moreover, these methods mainly employed macroscale features with little attention to details, which degrades the classification accuracy, as rib fracture type is essentially determined by tiny fracture details. To address all these issues, we propose a novel framework to classify rib fractures, where we first introduce a multi-scale network to integrate multiple sizes of fractures to minimize size alteration, and further formulate fracture classification problem as a segmentation problem to enforce network attention to tiny fracture details, so as to increase the classification accuracy. Our method has been evaluated on a large dataset (with 53045 cases) with four types of fractures, including acute displaced fracture, acute non-displaced fracture, acute buckle fracture, and chronic fracture. The results are compared with state-of-the-art methods, which suggest that our proposed method achieves the best performance. The capability of our multi-scale segmentation strategy is also verified by experimental results, especially in handling huge size variation of rib fractures during fracture classification.