School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai South Road, Baiyun District, 510515, Guangzhou, China.
School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai South Road, Baiyun District, 510515, Guangzhou, China.
Comput Methods Programs Biomed. 2019 Oct;180:105014. doi: 10.1016/j.cmpb.2019.105014. Epub 2019 Aug 5.
In chest radiographs (CXRs), all bones and soft tissues are overlapping with each other, which raises issues for radiologists to read and interpret CXRs. Delineating the ribs and clavicles is helpful for suppressing them from chest radiographs so that their effects can be reduced for chest radiography analysis. However, delineating ribs and clavicles automatically is difficult by methods without deep learning models. Moreover, few of methods without deep learning models can delineate the anterior ribs effectively due to their faint rib edges in the posterior-anterior (PA) CXRs.
In this work, we present an effective deep learning method for delineating posterior ribs, anterior ribs and clavicles automatically using a fully convolutional DenseNet (FC-DenseNet) as pixel classifier. We consider a pixel-weighted loss function to mitigate the uncertainty issue during manually delineating for robust prediction.
We conduct a comparative analysis with two other fully convolutional networks for edge detection and the state-of-the-art method without deep learning models. The proposed method significantly outperforms these methods in terms of quantitative evaluation metrics and visual perception. The average recall, precision and F-measure are 0.773 ± 0.030, 0.861 ± 0.043 and 0.814 ± 0.023 respectively, and the mean boundary distance (MBD) is 0.855 ± 0.642 pixels of the proposed method on the test dataset. The proposed method also performs well on JSRT and NIH Chest X-ray datasets, indicating its generalizability across multiple databases. Besides, a preliminary result of suppressing the bone components of CXRs has been produced by using our delineating system.
The proposed method can automatically delineate ribs and clavicles in CXRs and produce accurate edge maps.
在胸部 X 光片（CXRs）中，所有的骨骼和软组织都相互重叠，这给放射科医生阅读和解释 CXRs 带来了问题。勾勒出肋骨和锁骨有助于抑制它们出现在胸部 X 光片中，从而减少它们对胸部 X 光分析的影响。然而，在没有深度学习模型的情况下，自动勾勒肋骨和锁骨是困难的。此外，由于后前位（PA）CXR 中肋骨边缘较淡，很少有无深度学习模型的方法能够有效地勾勒出前肋骨。
在这项工作中，我们提出了一种有效的深度学习方法，使用全卷积 DenseNet（FC-DenseNet）作为像素分类器自动勾勒出后肋骨、前肋骨和锁骨。我们考虑了一种像素加权损失函数，以减轻手动勾勒时的不确定性问题，从而实现稳健的预测。
我们对两种其他的用于边缘检测的全卷积网络和无深度学习模型的最先进方法进行了对比分析。在定量评估指标和视觉感知方面，所提出的方法明显优于这些方法。在测试数据集上，所提出的方法的平均召回率、精度和 F1 分数分别为 0.773±0.030、0.861±0.043 和 0.814±0.023，平均边界距离（MBD）为 0.855±0.642 像素。该方法在 JSRT 和 NIH 胸部 X 射线数据集上也表现良好，表明其在多个数据库中的通用性。此外，还使用我们的勾勒系统生成了抑制 CXR 骨骼成分的初步结果。
所提出的方法可以自动勾勒出 CXR 中的肋骨和锁骨，并生成准确的边缘图。
