Abstract
Prediction of the chronological age based on neuroimaging data is important for brain development analysis and brain disease diagnosis. Although many researches have been conducted for age prediction of older children and adults, little work has been dedicated to infants. To this end, this paper focuses on predicting infant age from birth to 2-year old using brain MR images, as well as identifying some related biomarkers. However, brain development during infancy is too rapid and heterogeneous to be accurately modeled by the conventional regression models. To address this issue, a two-stage prediction method is proposed. Specifically, our method first roughly predicts the age range of an infant and then finely predicts the accurate chronological age based on a learned, age-group-specific regression model. Combining this two-stage prediction method with another complementary one-stage prediction method, a hierarchical rough-to-fine (HRtoF) model is built. HRtoF effectively splits the rapid and heterogeneous changes during a long time period into several short time ranges and further mines the discrimination capability of cortical features, thus reaching high accuracy in infant age prediction. Taking 8 types of cortical morphometric features from structural MRI as predictors, the effectiveness of our proposed HRtoF model is validated using an infant dataset including 50 healthy subjects with 251 longitudinal MRI scans from 14 to 797 days. Comparing with five state-of-the-art regression methods, HRtoF model reduces the mean absolute error of the prediction from >48 days to 32.1 days. The correlation coefficient of the predicted age and the chronological age reaches 0.963. Moreover, based on HRtoF, the relative contributions of the eight types of cortical features for age prediction are also studied.
| Original language | English |
|---|---|
| Article number | 8632694 |
| Pages (from-to) | 214-225 |
| Number of pages | 12 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2020 Jan |
Keywords
- Cortical features
- infant age prediction
- longitudinal development
- machine learning
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
Cite this
Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features. / Hu, Dan; Wu, Zhengwang; Lin, Weili; Li, Gang; Shen, Dinggang.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 1, 8632694, 01.2020, p. 214-225.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features
AU - Hu, Dan
AU - Wu, Zhengwang
AU - Lin, Weili
AU - Li, Gang
AU - Shen, Dinggang
PY - 2020/1
Y1 - 2020/1
N2 - Prediction of the chronological age based on neuroimaging data is important for brain development analysis and brain disease diagnosis. Although many researches have been conducted for age prediction of older children and adults, little work has been dedicated to infants. To this end, this paper focuses on predicting infant age from birth to 2-year old using brain MR images, as well as identifying some related biomarkers. However, brain development during infancy is too rapid and heterogeneous to be accurately modeled by the conventional regression models. To address this issue, a two-stage prediction method is proposed. Specifically, our method first roughly predicts the age range of an infant and then finely predicts the accurate chronological age based on a learned, age-group-specific regression model. Combining this two-stage prediction method with another complementary one-stage prediction method, a hierarchical rough-to-fine (HRtoF) model is built. HRtoF effectively splits the rapid and heterogeneous changes during a long time period into several short time ranges and further mines the discrimination capability of cortical features, thus reaching high accuracy in infant age prediction. Taking 8 types of cortical morphometric features from structural MRI as predictors, the effectiveness of our proposed HRtoF model is validated using an infant dataset including 50 healthy subjects with 251 longitudinal MRI scans from 14 to 797 days. Comparing with five state-of-the-art regression methods, HRtoF model reduces the mean absolute error of the prediction from >48 days to 32.1 days. The correlation coefficient of the predicted age and the chronological age reaches 0.963. Moreover, based on HRtoF, the relative contributions of the eight types of cortical features for age prediction are also studied.
AB - Prediction of the chronological age based on neuroimaging data is important for brain development analysis and brain disease diagnosis. Although many researches have been conducted for age prediction of older children and adults, little work has been dedicated to infants. To this end, this paper focuses on predicting infant age from birth to 2-year old using brain MR images, as well as identifying some related biomarkers. However, brain development during infancy is too rapid and heterogeneous to be accurately modeled by the conventional regression models. To address this issue, a two-stage prediction method is proposed. Specifically, our method first roughly predicts the age range of an infant and then finely predicts the accurate chronological age based on a learned, age-group-specific regression model. Combining this two-stage prediction method with another complementary one-stage prediction method, a hierarchical rough-to-fine (HRtoF) model is built. HRtoF effectively splits the rapid and heterogeneous changes during a long time period into several short time ranges and further mines the discrimination capability of cortical features, thus reaching high accuracy in infant age prediction. Taking 8 types of cortical morphometric features from structural MRI as predictors, the effectiveness of our proposed HRtoF model is validated using an infant dataset including 50 healthy subjects with 251 longitudinal MRI scans from 14 to 797 days. Comparing with five state-of-the-art regression methods, HRtoF model reduces the mean absolute error of the prediction from >48 days to 32.1 days. The correlation coefficient of the predicted age and the chronological age reaches 0.963. Moreover, based on HRtoF, the relative contributions of the eight types of cortical features for age prediction are also studied.
KW - Cortical features
KW - infant age prediction
KW - longitudinal development
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85077669822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077669822&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2019.2897020
DO - 10.1109/JBHI.2019.2897020
M3 - Article
C2 - 30716056
AN - SCOPUS:85077669822
VL - 24
SP - 214
EP - 225
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 1
M1 - 8632694
ER -