Semi-supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency

Semi-supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency

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© 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, man...

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Bibliographic Details
Main Authors: Xu, Junshen (Author), Lala, Sayeri (Author), Gagoski, Borjan Aleksandar (Author), Abaci Turk, E (Author), Grant, PE (Author), Golland, Polina (Author), Adalsteinsson, Elfar (Author)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor)
Format: Article
Language:English
Published: Springer International Publishing, 2022-01-07T15:33:40Z.
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