Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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SVoRT: Iterative Transformer for?Slice-to-Volume Registration in?Fetal Brain MRI motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the atte

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Double-Uncertainty Guided Spatial and?Temporal Consistency Regularization Weighting for?Learning-Base solution space. For most learning-based registration approaches, the regularization usually has a fixed weight and only constrains the spatial transformation. Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a s

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On the?Dataset Quality Control for?Image Registration Evaluationnnotations is crucial for unbiased comparisons because registration algorithms are trained and tested using these landmarks. Even though some data providers claim to have mitigated the inter-observer variability by having multiple raters, quality control such as a third-party screening can still be

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Embedding Gradient-Based Optimization in?Image Registration Networksimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional iterative energy optimization-based registration and network-based registration, and propose Gradient Descent Network for Image Registration (GraDIRN). Our proposed approach trains a DL network that emb

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ContraReg: Contrastive Learning of?Multi-modality Unsupervised Deformable Image Registration-modality registration techniques maximize hand-crafted inter-domain similarity functions, are limited in modeling nonlinear intensity-relationships and deformations, and may require significant re-engineering or underperform on new tasks, datasets, and domain pairs. This work presents ContraReg, an

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Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for?Deformable Medical Image Registration Ue-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embed

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Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learningmoving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative de