To develop an automated pulmonary fibrosis (PF) segmentation methodology using a 3D multi-scale convolutional encoder-decoder approach following the robust atlas-based active volume model in thoracic CT for Rhesus Macaques with radiation-induced lung damage. 152 thoracic computed tomography scans of Rhesus Macaques with radiation-induced lung damage were collected. The 3D input data are randomly augmented with the Gaussian blurring when applying the 3D multi-scale convolutional encoder-decoder (3D MSCED) segmentation method.PF in each scan was manually segmented in which 70% scans were used as training data, 20% scans were used as validation data, and 10% scans were used as testing data. The performance of the method is assessed based on a10-fold cross validation method. The workflow of the proposed method has two parts. First, the compromised lung volume with acute radiation-induced PF was segmented using a robust atlas-based active volume model. Next, a 3D multi-scale convolutional encoder-decoder segmentation method was developed which merged the higher spatial information from low-level features with the high-level object knowledge encoded in upper network layers. It included a bottom-up feed-forward convolutional neural network and a top-down learning mask refinement process. The quantitative results of our segmentation method achieved mean Dice score of (0.769, 0.853), mean accuracy of (0.996, 0.999), and mean relative error of (0.302, 0.512) with 95% confidence interval. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance in testing data. This method was extensively validated in NHP datasets. The results demonstrated that the approach is more robust relative to PF than other methods. It is a general framework which can easily be applied to segmentation other lung lesions.

要开发一种自动3D多尺度卷积编码器/解码器方法进行肺纤维化（PF）分割方法，该方法应遵循基于胸部CT的健壮的基于图谱的活动体积模型，对猕猴进行辐射诱发的肺损伤。收集了152例猕猴的胸部计算机X线断层扫描，并显示了辐射诱发的肺损伤。应用3D多尺度卷积编码器/解码器（3D MSCED）分割方法时，3D输入数据会随机进行高斯模糊处理。每次扫描中的PF均手动分割，其中70％的扫描用作训练数据，20％的扫描用作验证数据，而10％扫描用作测试数据。该方法的性能基于10倍交叉验证方法进行评估。该方法的工作流程分为两部分。第一，使用基于图谱的有效体积模型对急性放射诱发的PF受损的肺体积进行了细分。接下来，开发了一种3D多尺度卷积编码器/解码器分割方法，该方法将来自低层特征的较高空间信息与在较高网络层中编码的高层对象知识进行合并。它包括自下而上的前馈卷积神经网络和自上而下的学习蒙版细化过程。我们的分割方法的定量结果以95％的置信区间实现了平均Dice得分（0.769，0.853），平均准确度（0.996，0.999）和平均相对误差（0.302，0.512）。定性和定量比较表明，我们提出的方法可以实现更好的分割精度，并且测试数据的差异较小。该方法已在NHP数据集中得到了广泛验证。结果表明，相对于PF，该方法比其他方法更健壮。这是一个通用框架，可以轻松地用于分割其他肺部病变。