# Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

arXiv: https://arxiv.org/abs/2006.12704 · HTML: https://mlsanity.com/paper/2006.12704/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08763/) (2022)
- [Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation](https://mlsanity.com/paper/2203.10196/) (2022)
- [FetMRQC: a robust quality control system for multi-centric fetal brain MRI](https://mlsanity.com/paper/2311.04780/) (2023)
- [FetMRQC: Automated Quality Control for fetal brain MRI](https://mlsanity.com/paper/2304.05879/) (2023)
- [Expectation Maximization Pseudo Labels](https://mlsanity.com/paper/2305.01747/) (2023)
- [MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels](https://mlsanity.com/paper/2110.12179/) (2021)
- [Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series](https://mlsanity.com/paper/2310.03870/) (2023)
- [Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images](https://mlsanity.com/paper/2310.08805/) (2023)
- [Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction](https://mlsanity.com/paper/2503.10156/) (2025)
- [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023)

## Abstract (arXiv)

Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so image volumes are degraded by inter- and intra- slice motion artifacts. Besides, manual annotation for fetal MR image quality assessment are usually time-consuming. Therefore, in this work, a semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. Our method is based on the mean teacher model, where we not only enforce consistency between student and teacher models on the whole image, but also adopt an ROI consistency loss to guide the network to focus on the brain region. The proposed method is evaluated on a fetal brain MR dataset with 11,223 labeled images and more than 200,000 unlabeled images. Results show that compared with supervised learning, the proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods. The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.