Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs
Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs
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DOI：
10.1016/j.jvir.2019.05.026
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发表时间：
2020-01-01
影响因子：
2.9
通讯作者：
Wang, David S.
中科院分区：
文献类型：
--
作者：
Ni, Jason C.;Shpanskaya, Katie;Wang, David S.
目的：验证基于深度学习卷积神经网络(CNN)分类模型自动识别不同类型下腔静脉滤器的可行性和性能。材料和方法：从登记在单中心下腔静脉滤器登记处的患者中收集14种下腔静脉滤器的1375张裁剪后的X线片图像，139张图像作为测试集，其余图像用于训练和验证分类模型。改变图像的亮度、对比度、强度和旋转来增加训练集。具有固定预训练权重的50层ResNet体系结构是使用50个时期的软边际损失进行训练的。结果：CNN分类模型对整个测试集的F1得分为0.97(0.92-0.99)，对于14种单独的过滤器类型中的10种达到了1.00。在139个测试集图像中，有4个(2.9%)被错误识别，都被误认为是其他看起来非常相似的滤镜类型。热图阐明了模型用于分类预测的每种滤器类型的显著特征。结论：成功地开发了CNN分类模型，以识别X线片上14种类型的下腔静脉滤器，并显示出高性能。在潜在的实际应用之前，有必要对该模型进行进一步的改进和测试。
Purpose: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs.Materials and Methods: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set.Results: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction.Conclusions: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.