Precise proximal femur fracture classification for interactive training and surgical planning
Precise proximal femur fracture classification for interactive training and surgical planning
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DOI：
10.1007/s11548-020-02150-x
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发表时间：
2020-04-25
影响因子：
3
通讯作者：
Mateus, Diana
中科院分区：
文献类型：
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作者：
Jimenez-Sanchez, Amelia;Kazi, Anees;Mateus, Diana
目的证明基于深度学习的全自动计算机辅助诊断（CAD）工具的可行性，该工具根据AO分类在X射线图像上对股骨近端骨折进行定位和分类。拟议的框架旨在改善患者治疗计划，并为创伤外科住院医师的培训提供支持。材料与方法收集了1347例临床放射学研究的数据库。放射科医生和创伤外科医生用边界框注释所有骨折，并根据AO标准提供分类。在所有实验中，将数据集按患者分为三部分，比例为70%：10%：20%，分别构建训练集、验证集和测试集。ResNet-50和AlexNet架构分别实现为深度学习分类和本地化模型。准确度、精确度、召回率和F1分数被报告为分类指标。检索相似的情况下进行了评估的精度和召回。结果所提出的CAD工具用于将X线片分类为“A”、“B”和“未骨折”，其F1评分为87%，AUC为0.95。当对骨折与非骨折病例进行分类时，它提高了94%和0.98。骨折的先前定位导致相对于全图像分类的改进。总的来说，100%的感兴趣区域的预测中心包含在手动提供的边界框中。系统在10个病例中平均检索9个相关图像（来自同一类）。结论我们的CAD方案可以定位、检测并进一步分类股骨近端骨折，其结果可与专家级别和最先进的性能相媲美。我们的辅助定位模型高度准确地预测了X光片中的感兴趣区域。我们进一步研究了几种验证策略，以将其纳入日常临床常规。作为临床使用案例的ROI和图像检索的大小的灵敏度分析。
Purpose Demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. Material and methods A database of 1347 clinical radiographic studies was collected. Radiologists and trauma surgeons annotated all fractures with bounding boxes and provided a classification according to the AO standard. In all experiments, the dataset was split patient-wise in three with the ratio 70%:10%:20% to build the training, validation and test sets, respectively. ResNet-50 and AlexNet architectures were implemented as deep learning classification and localization models, respectively. Accuracy, precision, recall and F1-score were reported as classification metrics. Retrieval of similar cases was evaluated in terms of precision and recall. Results The proposed CAD tool for the classification of radiographs into types "A," "B" and "not-fractured" reaches a F1score of 87% and AUC of 0.95. When classifying fractures versus not-fractured cases it improves up to 94% and 0.98. Prior localization of the fracture results in an improvement with respect to full-image classification. In total, 100% of the predicted centers of the region of interest are contained in the manually provided bounding boxes. The system retrieves on average 9 relevant images (from the same class) out of 10 cases. Conclusion Our CAD scheme localizes, detects and further classifies proximal femur fractures achieving results comparable to expert-level and state-of-the-art performance. Our auxiliary localization model was highly accurate predicting the region of interest in the radiograph. We further investigated several strategies of verification for its adoption into the daily clinical routine. A sensitivity analysis of the size of the ROI and image retrieval as a clinical use case were presented.