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Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks

Authors
Liu, SiyuanThung, Kim-HanLin, WeiliYap, Pew-ThianShen, Dinggang
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Training; Semisupervised learning; Standards; Quality assessment; Annotations; Deep learning; Kernel; Image quality assessment; nonlocal residual networks; semi-supervised learning; self-training
Citation
IEEE TRANSACTIONS ON IMAGE PROCESSING, v.29, pp.7697 - 7706
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume
29
Start Page
7697
End Page
7706
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/59003
DOI
10.1109/TIP.2020.2992079
ISSN
1057-7149
Abstract
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with near-perfect accuracy.
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