School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 215000, China.
Comput Math Methods Med. 2022 Aug 18;2022:8945423. doi: 10.1155/2022/8945423. eCollection 2022.
Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net's skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.
肋骨骨折是胸部创伤引起的常见损伤，可能会导致严重后果。准确诊断肋骨骨折至关重要。低剂量胸部计算机断层扫描（CT）常用于肋骨骨折的诊断，近年来基于卷积神经网络（CNN）的方法已经辅助医生进行肋骨骨折诊断。然而，由于肋骨骨折数据缺乏以及肋骨骨折形状不规则、多样，基于 CNN 的方法难以提取肋骨骨折特征。因此，在检测肋骨骨折的准确性和灵敏度方面，它们无法达到令人满意的效果。受注意力机制的启发，我们提出了用于肋骨骨折检测的 CFSG U-Net。CSFG U-Net 使用 U-Net 架构，并通过双注意力模块得到增强，包括通道融合注意力模块（CFAM）和空间分组注意力模块（SGAM）。CFAM 使用通道注意力机制沿通道维度重新加权特征图，并细化 U-Net 的跳过连接。SGAM 使用分组技术生成空间注意力以调整空间维度的特征图，使空间注意力模块能够捕获更细粒度的语义信息。为了评估我们提出的方法的有效性，我们在研究中建立了一个肋骨骨折数据集。我们在数据集上的实验结果表明，我们提出的方法的最大灵敏度为 89.58%，平均 FROC 评分为 81.28%，优于现有的肋骨骨折检测方法和注意力模块。
