Summary of Neureg: Domain-invariant 3d Image Registration on Human and Mouse Brains, by Taha Razzaq et al.
NeuReg: Domain-invariant 3D Image Registration on Human and Mouse Brains
by Taha Razzaq, Asim Iqbal
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM)
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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 the realm of medical brain imaging, precise image registration is crucial for mapping brain features accurately. Recent advances in deep learning have showcased remarkable performance in this area. However, existing models often falter when dealing with diverse 3D brain volumes due to variations in structure and contrast. To address this limitation, we introduce NeuReg, a novel Neuro-inspired architecture designed for domain-agnostic image registration. NeuReg leverages the feature of domain invariance by generating representations of imaging features that are agnostic to specific domains. This allows our model to capture variations across brain imaging modalities and species. We demonstrate state-of-the-art performance on multi-domain publicly available datasets, comprising human and mouse 3D brain volumes. Our findings reveal that NeuReg outperforms existing deep learning-based image registration models, particularly when trained on a ‘source-only’ domain and tested on an unseen target domain. This work establishes a new benchmark for domain-agnostic 3D brain image registration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to put together a puzzle with many different pieces that are all slightly different shapes and sizes. That’s kind of like what doctors do when they try to map the structure of the brain using medical images. They need to make sure that all the different parts fit together correctly. Recent advances in computer learning have helped improve this process, but there are still challenges. For example, it can be hard to match up images from different people or animals. To overcome these challenges, researchers created a new model called NeuReg. This model is able to create a “map” of the brain that works for many different types of images, even if they come from different sources. The results are very promising and could help doctors create more accurate maps of the brain. |
Keywords
* Artificial intelligence * Deep learning
