Multi-stream and multi-scale fusion rib fracture segmentation network based on UXNet.

Background: Accurate segmentation of rib fractures represents a pivotal procedure within surgical interventions. This meticulous process not only mitigates the likelihood of postoperative complications but also facilitates expedited patient recuperation. However, rib fractures in computed tomography (CT) images exhibit an uneven morphology and are not fixed in position, posing difficulties in segmenting fractures. This study aims to enhance the accuracy of elongated rib fracture segmentation, ultimately improving the efficiency of clinical diagnosis. Methods: In this study, we propose multi-stream and multi-scale fusion network based on efficient attention UXNet (M2SUXNet). It aims to enhance the segmentation accuracy of elongated rib fractures through multi-scale fusion attention enhancement. Firstly, we propose the multi-stream and multi-scale fusion (M2SF) module in the feature extraction stage. The module is designed with two parallel paths. Each path analyzes the image content using a different feature level. Then, the module effectively distinguishes the more critical feature information in the channel according to the feature weight ratio. The M2SF module integrates information from different scales to obtain comprehensive information on global and local features, achieving a more diverse feature representation. Secondly, the efficient attention (EA) module combines different channel information of input features to integrate channel and spatial features of different channels. The module better combines the context information, establishes the dependency between the space and the channel, enhances the focusing ability of the network on the fractures of different shapes, and improves the segmentation accuracy. Thirdly, the joint loss function of BCE with Logits Loss and Dice Loss is used to solve the sample imbalance problem. Results: We verified the effectiveness of the proposed model on the public RibFrac dataset. The experimental results demonstrated that the model achieved a Dice coefficient of 75.34%, a joint intersection over union (IoU) of 60.44%, and a precision of 93.79%. Conclusions: The proposed model for rib fracture segmentation has higher accuracy and feasibility than other existing models. Besides, the M2SUXNet can effectively improve the segmentation performance of elongated rib fractures.