论文标题
宾果游戏：贝叶斯内在的groupwise通过显式层次分离
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement
论文作者
论文摘要
多模式组登记将一组医学图像中的内部结构对齐。当前解决此问题的方法涉及在所有图像的关节强度概况上制定相似性度量，这对于大图像组可能在计算上均高，并且在各种条件下都不稳定。为了解决这些问题，我们提出了基于深度学习的一般无监督的分层贝叶斯框架宾果游戏，以学习固有的结构表示，以衡量多模式图像的相似性。尤其是，提出了具有新型后验的各种自动编码器，该自动编码器促进了结构表示和空间变换的分离学习，并表征了与形状过渡和外观变化的共同结构的成像过程。值得注意的是，宾果游戏可以从小组中学习，而对大规模的群体式注册进行了测试，从而大大降低了计算成本。我们将宾果游戏与三种公共主体内和受试者间数据集的五种迭代或深度学习方法进行了比较，即，官员，心脏的MS-CMR和Learn2reg Abdomen MR-CT，并证明了其出色的准确性和计算效率，甚至显示了非常大的组尺寸（例如，在每个组中的1300 2D图像中）。
Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group).
