Multi-Contrast MRI Segmentation Trained on Synthetic Images
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Abstract (arXiv)
In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train an image segmentation engine. We showed promising segmentation results tested on real multi-contrast MRI scans when delineating muscle, fat, bone and bone marrow, all trained on synthetic images. Based on synthetic image training, our segmentation results were as high as 93.91%, 94.11%, 91.63%, 95.33%, for muscle, fat, bone, and bone marrow delineation, respectively. Results were not significantly different from the ones obtained when real images were used for segmentation training: 94.68%, 94.67%, 95.91%, and 96.82%, respectively.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 6 Jul 2022.
Builds on
7 of 13 references.
References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
- 2019Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Networkcited by 98
- 2020Capsules for Biomedical Image Segmentationcited by 18
- 2020Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRIcited by 3
- 2018A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentationcited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.