# Multi-Contrast MRI Segmentation Trained on Synthetic Images

arXiv: https://arxiv.org/abs/2207.02469 · HTML: https://mlsanity.com/paper/2207.02469/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network](https://mlsanity.com/paper/1904.12200/) (2019)
- [Capsules for Biomedical Image Segmentation](https://mlsanity.com/paper/2004.04736/) (2020)
- [Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI](https://mlsanity.com/paper/2009.03128/) (2020)
- [A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation](https://mlsanity.com/paper/1810.06071/) (2018)

## Built on by (most cited first, 2 of 2)

- [The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence](https://mlsanity.com/paper/2302.00225/) (2023)
- [Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation](https://mlsanity.com/paper/2506.23334/) (2025)

## 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.