Multi-stage Image Quality Assessment of Diffusion MRI via Semi-supervised Nonlocal Residual Networks

the UNC/UMN Baby Connectome Project Consortium

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fast and automated image quality assessment (IQA) for diffusion MR images is crucial so that a rescan decision can be made swiftly during or after the scanning session. However, learning this task is challenging as the number of annotated data is limited and the annotated label is not always perfect. To this end, we introduce an automatic multi-stage IQA method for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., slice-wise quality assessment (QA) using a nonlocal residual network, volume-wise QA by agglomerating the extracted features of slices belonging to one volume using a nonlocal network, and subject-wise QA by ensembling the QA results of volumes belonging to one subject. In addition, we employ semi-supervised learning to make full use of a small amount of annotated data and a large amount of unlabeled data to train our network. Specifically, we first pre-train our network using labeled data, which are iteratively expanded by labeling the unlabeled data with the trained network. Furthermore, we devise a self-training strategy which iteratively relabels and prunes the labeled dataset when training the network to deal with noisy labels. Experimental results demonstrate that our network, trained using only samples of modest size, exhibits great generalizability and is capable of conducting large-scale rapid IQA with near-perfect accuracy.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages521-528
Number of pages8
ISBN (Print)9783030322472
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    the UNC/UMN Baby Connectome Project Consortium (2019). Multi-stage Image Quality Assessment of Diffusion MRI via Semi-supervised Nonlocal Residual Networks. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 521-528). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_58