Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI
consistency
- URL: http://arxiv.org/abs/2006.12704v1
- Date: Tue, 23 Jun 2020 02:40:45 GMT
- Title: Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI
consistency
- Authors: Junshen Xu, Sayeri Lala, Borjan Gagoski, Esra Abaci Turk, P. Ellen
Grant, Polina Golland, Elfar Adalsteinsson
- Abstract summary: The current protocol for T2-weighted fetal brain MRI is not robust to motion.
A semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed.
The proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods.
- Score: 1.7757605747890044
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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