Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach
Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach
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DOI：
10.1016/j.ejrad.2020.109373
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发表时间：
2020-12-01
影响因子：
3.3
通讯作者：
Masse, Alessandro
中科院分区：
文献类型：
--
作者：
Tanzi, Leonardo;Vezzetti, Enrico;Masse, Alessandro
目的：疑似骨折是患者前往急诊室的最常见原因之一，通常很难在胶片扫描中检测和分析。因此，我们的目标是设计一个基于深度学习的工具，能够帮助医生诊断骨折，遵循由Arbeitsgemeinschaft fur Osteosynthesefragen（AO）基金会和骨科创伤协会（OTA）提出的分层分类。方法：对2453例股骨近端骨折图像进行人工标注，并对不同骨折类型进行分类（1133例未断裂股骨，570例A型，750例B型）。其次，将A型裂缝进一步划分为A1、A2、A3型。采取了两种办法：第一个是微调的InceptionV 3卷积神经网络（CNN），用作我们自己提出的方法的基线;第二个是由级联的连续CNN组成的多级架构，非常适合AO/OTA分类的分层结构。梯度类激活图（Grad-CAM）用于可视化图像的最相关区域以进行分类。CNN的平均能力通过准确率、受试者工作特征曲线下面积（AUC）、召回率、精确率和F1评分来测量。结果：3类分类的平均准确率为0.86（CI 0.84-0.88），5类分类的平均准确率为0.81（CI 0.79-0.82）。专家的平均准确性提高了14%，有和没有CAD（计算机辅助诊断）system.Conclusion：我们显示了使用基于CNN的CAD系统提高诊断准确性和帮助专业知识水平较低的学生的潜力。我们从股骨近端骨折开始工作，我们的目标是在未来将其扩展到所有骨段，以便实现一种可用于日常医院常规的工具。
Purpose: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doctors in diagnosis of bone fractures, following the hierarchical classification proposed by the Arbeitsgemeinschaft fur Osteosynthesefragen (AO) Foundation and the Orthopaedic Trauma Association (OTA).Methods: 2453 manually annotated images of proximal femur were used for the classification in different fracture types (1133 Unbroken femur, 570 type A, 750 type B). Secondly, the A type fractures were further classified into the types A1, A2, A3. Two approaches were implemented: the first is a fine-tuned InceptionV3 convolutional neural network (CNN), used as a baseline for our own proposed approach; the second is a multistage architecture composed by successive CNNs in cascade, perfectly suited to the hierarchical structure of the AO/OTA classification. Gradient Class Activation Maps (Grad-CAM) where used to visualize the most relevant areas of the images for classification. The averaged ability of the CNN was measured with accuracy, area under receiver operating characteristics curve (AUC), recall, precision and F1-score. The averaged ability of the orthopedists with and without the help of the CNN was measured with accuracy and Cohen's Kappa coefficient.Results: We obtained an averaged accuracy of 0.86 (CI 0.84-0.88) for three classes classification and 0.81 (CI 0.79-0.82) for five classes classification. The average accuracy improvement of specialists was 14 % with and without the CAD (Computer Assisted Diagnosis) system.Conclusion: We showed the potential of using a CAD system based on CNN for improving diagnosis accuracy and for helping students with a lower level of expertise. We started our work with proximal femur fractures and we aim to extend it to all bone segments further in the future, in order to implement a tool that could be used in every-day hospital routine.