TY - GEN
T1 - Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network
AU - Guan, Hao
AU - Yang, Erkun
AU - Wang, Li
AU - Yap, Pew Thian
AU - Liu, Mingxia
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092696624&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59861-7_12
DO - 10.1007/978-3-030-59861-7_12
M3 - Conference contribution
AN - SCOPUS:85092696624
SN - 9783030598600
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 119
BT - Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
A2 - Cao, Xiaohuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th 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
Y2 - 4 October 2020 through 4 October 2020
ER -