Summary of Iguane: a 3d Generalizable Cyclegan For Multicenter Harmonization Of Brain Mr Images, by Vincent Roca et al.
IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images
by Vincent Roca, Grégory Kuchcinski, Jean-Pierre Pruvo, Dorian Manouvriez, Renaud Lopes
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed paper introduces a novel deep learning model called IGUANe, which aims to harmonize magnetic resonance imaging (MRI) data from multiple acquisition sites. This is achieved by leveraging the strengths of domain translation and style transfer methods for multicenter brain MRI image harmonization. The IGUANe framework extends CycleGAN by integrating an arbitrary number of domains for training through a many-to-one architecture, enabling the implementation of sampling strategies that prevent confusion between site-related and biological variabilities. During inference, the model can be applied to any image, even from an unknown acquisition site, making it a universal generator for harmonization. The proposed framework was evaluated on a dataset comprising T1-weighted images from 11 different scanners, with assessments including the transformation of MR images with traveling subjects, the preservation of pairwise distances between MR images within domains, and the performance in age regression and patient classification tasks. The results suggest that IGUANe better preserves individual information in MR images and is more suitable for maintaining and reinforcing variabilities related to age and Alzheimer’s disease. The proposed framework has potential applications in various multicenter contexts and may be further assessed in future studies using different image modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IGUANe is a new way to make MRI images from different places look the same, so that they can be used together for better analysis. This is important because MRI machines are not all the same, and this difference can affect how well we understand what the images mean. IGUANe uses special computer algorithms to translate and harmonize MRI images from different sites, making it easier to compare them. The researchers tested IGUANe on a big dataset of MRI scans from 11 different machines and found that it worked really well. They also showed that IGUANe can preserve the unique details in each image and help us understand how age affects brain health. This new tool has lots of potential for helping doctors and scientists work with MRI images more effectively. |
Keywords
* Artificial intelligence * Classification * Deep learning * Inference * Regression * Style transfer * Translation
