Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration.
Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration.
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DOI：
10.1007/978-3-030-59716-0_22
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Jagadeesan J
中科院分区：
文献类型：
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作者：
Xu Z;Luo J;Yan J;Pulya R;Li X;Wells W 3rd;Jagadeesan J
计算机断层扫描（CT）图像和磁共振（MR）成像之间的可变形图像配准对于许多图像引导治疗是必不可少的。本文提出了一种新的基于投影的无监督可变形图像配准方法。与试图将多模态问题（例如，CT到MR）转化为单峰问题（例如，通过图像到图像的转换，我们的方法利用从以下两者估计的变形场：（i）转换的MR图像和（ii）以双流方式的原始CT图像，并自动学习如何融合它们以实现更好的配准性能。多模态配准网络可以通过计算上有效的相似性度量来有效地训练，而无需任何地面真实变形。我们的方法已经在两个临床数据集上进行了评估，与最先进的传统方法和基于学习的方法相比，结果令人鼓舞。
Deformable image registration between Computed Tomography (CT) images and Magnetic Resonance (MR) imaging is essential for many image-guided therapies. In this paper, we propose a novel translation-based unsupervised deformable image registration method. Distinct from other translation-based methods that attempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodal problem (e.g., MR-to-MR) via image-to-image translation, our method leverages the deformation fields estimated from both: (i) the translated MR image and (ii) the original CT image in a dual-stream fashion, and automatically learns how to fuse them to achieve better registration performance. The multimodal registration network can be effectively trained by computationally efficient similarity metrics without any ground-truth deformation. Our method has been evaluated on two clinical datasets and demonstrates promising results compared to state-of-the-art traditional and learning-based methods.