论文标题
具有深度学习的新生儿X光片中多个导管的自动分类
Automatic classification of multiple catheters in neonatal radiographs with deep learning
论文作者
论文摘要
我们开发和评估一种深度学习算法，以对新生儿胸部和腹部X光片进行多个导管进行分类。使用777个新生儿胸部和腹部X光片的数据集对卷积神经网络（CNN）进行训练，分别用于训练验证测试的81％-9％-10％。我们采用了Resnet-50（CNN），并在Imagenet上进行了预训练。地面真相标签仅限于标记每个图像，以指示内气管（ETT），鼻腔胃管（NGTS）以及脐动脉和静脉导管（UACS，UACS，UVCS）的存在或不存在。数据集包括561张图像，其中包含2个或更多导管，167张图像，只有1个图像，没有49个图像。通过平均精度（AP）测量性能，根据Precision-Recall曲线的面积计算得出。在我们的测试数据上，NGTS的总体AP（95％置信区间）为0.977（0.679-0.999），ETT的算法为0.989（0.751-1.000），为0.979（0.873-0.997），UACS，UACS，0.937（0.937）（0.937）（0.937）（0.937（0.937）（0.78555-0.984.984）。 58个测试图像的表现相似，由2个或更多导管组成，NGTS的AP为0.975（0.255-1.000），ETTS的0.997（0.009-1.000），0.981（0.797-0.998），UAC，UAC和0.937（0.937）（0.937（0.689-99-999990），均为UVCC。因此，我们的网络在对这四种导管类型的同时检测中实现了强劲的性能。放射科医生可以将这种算法用作节省时间的机制来自动化射线照片的导管报告。
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The data set included 561 images containing 2 or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of 2 or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.
