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Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
One of the fundamental challenges in supervised learning for multimodal ...
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MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation
Current deep-learning-based registration algorithms often exploit intens...
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Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
Image registration with deep neural networks has become an active field ...
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Locate, Segment and Match: A Pipeline for Object Matching and Registration
Image registration requires simultaneous processing of multiple images t...
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Label-driven weakly-supervised learning for multimodal deformable image registration
Spatially aligning medical images from different modalities remains a ch...
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Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registra...
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Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales
This paper focuses on potential accuracy of remote sensing images regist...
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Conditional Segmentation in Lieu of Image Registration
Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detailed pixel- or voxel-level correspondence within or outside of these ROIs often have little clinical value. In this paper, we propose an alternative paradigm in which the location of corresponding image-specific ROIs, defined in one image, within another image is learnt. This results in replacing image registration by a conditional segmentation algorithm, which can build on typical image segmentation networks and their widely-adopted training strategies. Using the registration of 3D MRI and ultrasound images of the prostate as an example to demonstrate this new approach, we report a median target registration error (TRE) of 2.1 mm between the ground-truth ROIs defined on intraoperative ultrasound images and those propagated from the preoperative MR images. Significantly lower (>34 conditional segmentation compared with those obtained from a previously-proposed spatial-transformation-predicting registration network trained with the same multiple ROI labels for individual image pairs. We conclude this work by using a quantitative bias-variance analysis to provide one explanation of the observed improvement in registration accuracy.
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