完整
发表于 2025-3-30 08:14:53
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maculated
发表于 2025-3-30 16:20:29
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
过去分词
发表于 2025-3-30 19:11:59
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
geriatrician
发表于 2025-3-30 20:55:39
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Vertical
发表于 2025-3-31 03:15:56
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
fallible
发表于 2025-3-31 06:02:28
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Frequency
发表于 2025-3-31 12:43:30
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
没有希望
发表于 2025-3-31 15:58:48
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
思想
发表于 2025-3-31 17:33:59
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
博爱家
发表于 2025-4-1 01:04:57
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