Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.
PLoS One. 2018 Nov 16;13(11):e0207496. doi: 10.1371/journal.pone.0207496. eCollection 2018.
Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but their drawback is the need of large annotated datasets. Finetuning pre-trained networks is a viable alternative, but it is not clear a priori if training with small annotated datasets will be successful, as it depends on the problem at hand. In this paper, we show that pre-trained networks can be utilized to produce an effective detector of ossification areas in pediatric X-ray images of hands.
A publicly available Faster R-CNN network, pre-trained on the COCO dataset, was utilized and finetuned with 240 manually annotated radiographs from the RSNA Pediatric Bone Age Challenge, which comprises over 14.000 pediatric radiographs. The validation is done on another 89 radiographs from the dataset and the performance is measured by Intersection-over-Union (IoU). To understand the effect of the data size on the pre-trained network, subsampling was applied to the training data and the training was repeated. Additionally, the network was trained from scratch without any pre-trained weights. Finally, to understand whether the trained model could be useful, we compared the inference of the network to an annotation of an expert radiologist. The finetuned network was able to achieve an average precision (mAP@0.5IoU) of 92.92 ± 1.93. Apart from the wrist region, all ossification areas were able to benefit from more data. In contrast, the network trained from scratch was not able to produce any correct results. When compared to the annotations of the expert radiologist, the network was able to localize the regions quite well, as the F1-Score was on average 91.85 ± 1.06.
By finetuning a pre-trained deep neural network, with 240 annotated radiographs, we were able to successfully detect ossification areas in prediatric hand radiographs.
在手骨 X 射线图像中检测骨化区域是一项重要任务，例如在自动骨龄估计中作为预处理步骤。深度神经网络最近已成为事实上的标准检测方法，但它们的缺点是需要大型注释数据集。微调预训练网络是一种可行的替代方法，但尚不清楚使用小型注释数据集进行训练是否会成功，因为这取决于手头的问题。在本文中，我们展示了可以利用预训练网络对手部小儿 X 射线图像中的骨化区域生成有效的检测器。
利用了一个公开的 Faster R-CNN 网络，该网络在 COCO 数据集上进行了预训练，并使用了来自 RSNA 小儿骨龄挑战赛的 240 张手动注释射线照片进行了微调，该数据集包含了超过 14000 张小儿射线照片。在来自该数据集的另外 89 张射线照片上进行了验证，并通过交并比（IoU）来衡量性能。为了了解数据大小对预训练网络的影响，对训练数据进行了抽样，并重复了训练。此外，还在没有任何预训练权重的情况下从头开始训练网络。最后，为了了解训练的模型是否有用，我们将网络的推断与专家放射科医生的注释进行了比较。微调后的网络能够实现平均精度（mAP@0.5IoU）为 92.92±1.93。除了腕部区域外，所有骨化区域都可以从更多的数据中受益。相比之下，从头开始训练的网络无法产生任何正确的结果。与专家放射科医生的注释相比，网络能够很好地定位区域，平均 F1 分数为 91.85±1.06。
通过使用 240 张注释射线照片微调预训练的深度神经网络，我们成功地在手部小儿 X 射线图像中检测到骨化区域。
