Summary of Hierarchical Uncertainty Estimation For Learning-based Registration in Neuroimaging, by Xiaoling Hu et al.
Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
by Xiaoling Hu, Karthik Gopinath, Peirong Liu, Malte Hoffmann, Koen Van Leemput, Oula Puonti, Juan Eugenio Iglesias
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to estimating uncertainty in deep learning-based image registration methods for medical imaging and neuroimaging. The proposed framework exploits the spatial modeling peculiarities of these problems by propagating uncertainties estimated at local levels to global transformation models and downstream tasks. Specifically, the authors justify using Gaussian distributions for local uncertainty modeling and develop a hierarchical framework that spreads uncertainties across levels depending on transformation model choices. Experiments show that the proposed approach improves registration accuracy in brain MRI scans and enables uncertainty-aware fitting of transformations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve image registration methods used in medical imaging, specifically human neuroimaging with magnetic resonance imaging (MRI). The problem is that previous deep learning-based approaches didn’t properly estimate the uncertainty associated with these methods. This paper proposes a new way to calculate and use this uncertainty to get better results. The authors test their approach on publicly available data sets and show that it improves registration accuracy in brain MRI scans. |
Keywords
» Artificial intelligence » Deep learning
