A Deep Learning Framework for Noise Component Detection from Resting-State Functional MRI

for UNC/UMN Baby Connectome Project Consortium

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

1 Citation (Scopus)

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional imaging technique that has been widely used to investigate brain functional connectome. Noises and artifacts are dominant in the raw rs-fMRI, making effective noise removal a necessity prior to any subsequent analysis. Without requiring any additional biophysiological recording devices, directly applying independent component analysis on rs-fMRI data becomes a popular process further separating structured noise from signals. However, fast and accurate automatic identification of the noise-related components is critical. Conventional machine learning techniques have been used in training such a classifier with manually engineered features of the components, which usually takes a long time even in the testing phase because its success relies on exhaustively extraction of spatial and temporal features and assembling multiple complicated classifiers to reach satisfactory results. In this paper, we proposed a novel, end-to-end, deep learning-based framework dedicated for noise component identification via effective, automatic, multilayer, hierarchically embedded feature extraction. The merit that does not require any assumptions on the features guarantees its unprecedented performance on the rs-fMRI data even from very heterogeneous cohorts. The speed of this method can be further accelerated due to its inherent ability of parallel computing with GPU. We validate our method with a challenging infant rs-fMRI dataset with high resolution and high quality, which are very different from the commonly used adult data. Our proposed method is more general, hypothesis-free, fast (<1 s for single component classification), and accurate (>97% 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
Pages754-762
Number of pages9
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

Keywords

  • Component identification
  • Convolutional neural networks
  • Deep learning
  • Denoising
  • Independent component analysis
  • Resting-state fMRI

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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