论文标题
使用深神经网络的跨模式多ATLAS分割
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
论文作者
论文摘要
多ATLAS分割（MAS）中的图像注册和标记融合都取决于目标和地图集图像之间的强度相似性。但是，当使用不同的成像协议获取目标和地图集图像时，这种相似性可能是有问题的。高级结构信息可以在与深神经网络（DNNS）合作时为跨模式图像提供可靠的相似性测量。这项工作为跨模式图像提供了一个新的MAS框架，其中图像注册和标签融合都是DNNS实现的。对于图像注册，我们提出了一个一致的注册网络，该网络可以共同估计向前和向后密集的位移场（DDFS）。此外，在网络中采用了可逆约束来减少估计的DDF的对应关系。对于标签融合，我们适应了几个弹药的学习网络，以测量地图集和目标斑块的相似性。此外，网络可以无缝集成到基于补丁的标签融合中。提出的框架在MICCAI 2017的MM-WHS数据集上进行了评估。结果表明该框架在跨模式注册和分割方面都是有效的。
Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation.
