Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
| Autor: | Sayeri Lala, Elfar Adalsteinsson, Polina Golland, Esra Abaci Turk, Borjan Gagoski, P. Ellen Grant, Junshen Xu |
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| Rok vydání: | 2022 |
| Předmět: |
business.industry
Computer science Image quality Deep learning Supervised learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Semi-supervised learning Article 030218 nuclear medicine & medical imaging Fetal brain 03 medical and health sciences 0302 clinical medicine Consistency (statistics) Brain size Artificial intelligence business Focus (optics) 030217 neurology & neurosurgery |
| Zdroj: | Med Image Comput Comput Assist Interv Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597245 MICCAI (6) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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