4D-CT deformable image registration using multiscale unsupervised deep learning.
4D-CT deformable image registration using multiscale unsupervised deep learning.
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DOI：
10.1088/1361-6560/ab79c4
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发表时间：
2020-04-20
影响因子：
3.5
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Lei Y;Fu Y;Wang T;Liu Y;Patel P;Curran WJ;Liu T;Yang X
4D-CT图像的可变形图像配准(DIR)在软组织或基准标记物的运动跟踪、目标清晰度、图像融合、剂量累积和治疗反应评估等多种放射治疗应用中具有重要意义。由于4D-CT腹部图像的外观差异大、体积大，准确、快速地配准是非常具有挑战性的。在这项研究中，我们提出了一种准确、快速的多尺度DIR网络(MS-DIRNet)用于腹部4D-CT配准。MS-DIRNet由全球网络(GlobalNet)和本地网络(LocalNet)组成。GlobalNet使用下采样的整个图像体积进行训练，而LocalNet使用采样图像块进行训练。MS-DIRNet由一个生成器和一个鉴别器组成。训练生成器根据运动图像和目标图像直接预测变形矢量场。该生成器采用具有多个关注门的卷积神经网络实现。训练鉴别器以区分变形图像和目标图像，以提供额外的DVF正则化。MS-DIRNet的损失函数包括图像相似性损失、对抗性损失和DVF正则化损失三部分。MS-DIRNet以一种完全无人监督的方式进行培训，这意味着不需要基本事实DVF。与传统的DIR迭代计算DVF不同，MS-DIRNet能够在一次正演预测中计算最终DVF，这可以显著加快DIR过程。MS-DIRNet在25名患者的4D-CT数据集上进行了训练和测试，使用了五次交叉验证。为了评估配准的准确性，将MS-DIRNet的目标配准误差(TRES)与临床使用的软件进行了比较。结果显示，在4D-CT腹部DIR中，MS-DIRNet的平均TRE为1.2±0.8 mm，优于商业软件，平均TRE为2.5±0.8 mm，显示了我们的方法在基准标记追踪和整体软组织对齐方面的优越性能。
Deformable image registration (DIR) of 4D-CT images is important in multiple radiation therapy applications including motion tracking of soft tissue or fiducial markers, target definition, image fusion, dose accumulation and treatment response evaluations. It is very challenging to accurately and quickly register 4D-CT abdominal images due to its large appearance variances and bulky sizes. In this study, we proposed an accurate and fast multi-scale DIR network (MS-DIRNet) for abdominal 4D-CT registration. MS-DIRNet consists of a global network (GlobalNet) and local network (LocalNet). GlobalNet was trained using down-sampled whole image volumes while LocalNet was trained using sampled image patches. MS-DIRNet consists of a generator and a discriminator. The generator was trained to directly predict a deformation vector field (DVF) based on the moving and target images. The generator was implemented using convolutional neural networks with multiple attention gates. The discriminator was trained to differentiate the deformed images from the target images to provide additional DVF regularization. The loss function of MS-DIRNet includes three parts which are image similarity loss, adversarial loss and DVF regularization loss. The MS-DIRNet was trained in a completely unsupervised manner meaning that ground truth DVFs are not needed. Different from traditional DIRs that calculate DVF iteratively, MS-DIRNet is able to calculate the final DVF in a single forward prediction which could significantly expedite the DIR process. The MS-DIRNet was trained and tested on 25 patients’ 4D-CT datasets using five-fold cross validation. For registration accuracy evaluation, target registration errors (TREs) of MS-DIRNet were compared to clinically used software. Our results showed that the MS-DIRNet with an average TRE of 1.2 ± 0.8 mm outperformed the commercial software with an average TRE of 2.5 ± 0.8 mm in 4D-CT abdominal DIR, demonstrating the superior performance of our method in fiducial marker tracking and overall soft tissue alignment.
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影响因子：
3.8
作者：
Harms J;Lei Y;Wang T;Zhang R;Zhou J;Tang X;Curran WJ;Liu T;Yang X
通讯作者：
Yang X
DOI：
10.1146/annurev-bioeng-071516-044442
发表时间：
2017-06-21
影响因子：
9.7
作者：
Shen D;Wu G;Suk HI
通讯作者：
Suk HI
影响因子：
2.3
作者：
Oh S;Kim S
通讯作者：
Kim S
影响因子：
10.9
作者：
de Vos, Bob D.;Berendsen, Floris F.;Isgum, Ivana
通讯作者：
Isgum, Ivana
影响因子：
3.8
作者：
Brock, Kristy K.;Mutic, Sasa;Kessler, Marc L.
通讯作者：
Kessler, Marc L.