# Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation

arXiv: https://arxiv.org/abs/2111.04737 · HTML: https://mlsanity.com/paper/2111.04737/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 8 Nov 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset](https://mlsanity.com/paper/2010.15526/) (2020)
- [Automatic brain tissue segmentation in fetal MRI using convolutional neural networks](https://mlsanity.com/paper/1906.04713/) (2019)
- [A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI](https://mlsanity.com/paper/2004.12847/) (2020)
- [Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation](https://mlsanity.com/paper/2107.03846/) (2021)
- [Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels](https://mlsanity.com/paper/2009.06275/) (2020)
- [FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom](https://mlsanity.com/paper/2109.03624/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data](https://mlsanity.com/paper/2403.15103/) (2024)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes](https://mlsanity.com/paper/2308.06762/) (2023)
- [Domain generalization in fetal brain MRI segmentation with multi-reconstruction augmentation](https://mlsanity.com/paper/2211.14282/) (2022)

## Abstract (arXiv)

The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brain stem.