Semi-supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency
Name
2006.12704.pdf
Description
Submitted version
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2.06 MB
Format
Unknown
Checksum (MD5)
996c7185d2e0610445d47becdc1b2792
Author(s)
Xu, Junshen
Lala, Sayeri
Gagoski, Borjan Aleksandar
Abaci Turk, Esra
Grant, PE
Golland, Polina
Adalsteinsson, Elfar
Date Issued
2020
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Xu, J, Lala, S, Gagoski, B, Abaci Turk, E, Grant, PE et al. 2020. "Semi-supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12266 LNCS.
Version
Original manuscript
Abstract
© 2020, Springer Nature Switzerland AG. 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-030-59725-2_37
