TY - GEN
T1 - A preliminary volumetric mri study of amygdala and hippocampal subfields in autism during infancy
AU - Li, Guannan
AU - Chen, Meng Hsiang
AU - Li, Gang
AU - Wu, Di
AU - Sun, Quansen
AU - Shen, Dinggang
AU - Wang, Li
N1 - Funding Information:
This work was supported in part by National Institutes of Health grants MH109773, MH117943, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, MH116225, and MH107815. Data used in the preparation of this work were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. This work reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDAR.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Currently, autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Consequently, the window of opportunity for effective intervention may have passed, when the disorder is detected until 3 years of age. Thus, it is of great importance to identify imaging-based biomarkers for early diagnosis of ASD. Previous findings indicate that an abnormal pattern of the amygdala and hippocampal development in autism persists through childhood and adolescence. However, due to the low tissue contrast and small structural size of amygdala and hippocampal subfields, our knowledge on their growth in autistics in early stage still remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at around 24 months of age. Specifically, to address the challenge of low tissue contrast, we propose a novel deep-learning approach, i.e., dilated-dense U-Net, to automatically segment the amygdala and hippocampal subfields. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of segmentation accuracy. Our volume-based analysis shows the overgrowths of amygdala and CA1-3 of hippocampus, which may link to the emergence of autism spectrum disorder.
AB - Currently, autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Consequently, the window of opportunity for effective intervention may have passed, when the disorder is detected until 3 years of age. Thus, it is of great importance to identify imaging-based biomarkers for early diagnosis of ASD. Previous findings indicate that an abnormal pattern of the amygdala and hippocampal development in autism persists through childhood and adolescence. However, due to the low tissue contrast and small structural size of amygdala and hippocampal subfields, our knowledge on their growth in autistics in early stage still remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at around 24 months of age. Specifically, to address the challenge of low tissue contrast, we propose a novel deep-learning approach, i.e., dilated-dense U-Net, to automatically segment the amygdala and hippocampal subfields. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of segmentation accuracy. Our volume-based analysis shows the overgrowths of amygdala and CA1-3 of hippocampus, which may link to the emergence of autism spectrum disorder.
KW - Amygdala
KW - Early autism diagnosis
KW - Hippocampal subfields
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85073911742&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759439
DO - 10.1109/ISBI.2019.8759439
M3 - Conference contribution
AN - SCOPUS:85073911742
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1052
EP - 1056
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -