ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 26 citations in OpenAlex.
- A Systematic Review of Medical Image Quality Assessment.Journal of imaging · 2025Review
- Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series.Perinatal, preterm and paediatric image analysis (2025) · 2025Article
- MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations With Limited Labels.IEEE transactions on medical imaging · 2023Article
- NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI.IEEE transactions on medical imaging · 2023Article
- A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.Journal of imaging · 2022Article
- Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.Magnetic resonance in medicine · 2022Article
- Fetal Neuroimaging Updates.Magnetic resonance imaging clinics of North America · 2021Review
- Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.Journal of magnetic resonance imaging : JMRI · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.