Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Junshen Xu; Sayeri Lala; Borjan Gagoski; Esra Abaci Turk; P. Ellen Grant; Polina Golland; Elfar Adalsteinsson

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arXiv · 2006.12704

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

Junshen Xu·Sayeri Lala·Borjan Gagoski·Esra Abaci Turk·P. Ellen Grant·Polina Golland·Elfar Adalsteinsson

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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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Junshen Xu, Sayeri Lala, Borjan Gagoski, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson. 2020-06-23. Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency. https://arxiv.org/abs/2006.12704

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