Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network

Hao Guan, Erkun Yang, Li Wang, Pew Thian Yap, Mingxia Liu, Dinggang Shen

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

Abstract

Adolescent obesity has become a significant public health problem for the potential risk of various diseases in later life. Recent biomedical studies have revealed that obesity is associated with structural changes in the brain. Thus the computer-aided analysis of adolescent obesity based on brain MRI is of great clinical value. While previous methods typically rely on hand-crafted MRI features for obesity prediction, we propose to link adolescent obesity and brain MRI through a deep learning framework. The newly released brain MRI data from the large-scale Adolescent Brain Cognitive Development (ABCD) study has paved the way for such an exploration. In this paper, we propose a deep multi-cue regression network (DMRN) for MRI-based analysis of adolescent obesity. Specially, in DMRN, we first design a feature encoding network to automatically extract high-dimensional features from brain MR images, followed by a regression network to predict Body Mass Index (BMI) scores for obesity analysis. To take advantage of other prior knowledge of studied subjects, our DMRN framework further explicitly incorporates the demographic information (e.g., waist circumference) of subjects into the learning process. Experiments have been conducted on 3, 779 subjects with T1-weighted MRIs from the ABCD dataset. The results have provided some useful findings: (1) we consolidate the relationship between adolescent obesity and brain MRI as well as demographic information through a deep learning model; (2) we use visualization method to explain the prediction results by highlighting potential biomarkers in the brain MR images that are associated with adolescent obesity.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsMingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages111-119
Number of pages9
ISBN (Print)9783030598600
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period20/10/420/10/4

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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