Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164171
Title: Automated segmentation of visceral, deep subcutaneous, and superficial subcutaneous adipose tissue volumes in MRI of neonates and young children
Authors: Kway, Yeshe Manuel
Thirumurugan, Kashthuri
Tint, Mya Thway
Michael, Navin
Shek, Lynette Pei-Chi
Yap, Fabian Kok Peng
Tan, Kok Hian
Godfrey, Keith M.
Chong, Yap Seng
Fortier, Marielle Valerie
Marx, Ute C.
Eriksson, Johan G.
Lee, Yung Seng
Velan, S. Sendhil
Feng, Mengling
Sadananthan, Suresh Anand
Keywords: Science::Medicine
Issue Date: 2021
Source: Kway, Y. M., Thirumurugan, K., Tint, M. T., Michael, N., Shek, L. P., Yap, F. K. P., Tan, K. H., Godfrey, K. M., Chong, Y. S., Fortier, M. V., Marx, U. C., Eriksson, J. G., Lee, Y. S., Velan, S. S., Feng, M. & Sadananthan, S. A. (2021). Automated segmentation of visceral, deep subcutaneous, and superficial subcutaneous adipose tissue volumes in MRI of neonates and young children. Radiology: Artificial Intelligence, 3(5), e200304-. https://dx.doi.org/10.1148/ryai.2021200304
Project: NMRC/TCR/004-NUS/2008 
NMRC/TCR/012-NUHS/2014 
Journal: Radiology: Artificial intelligence
Abstract: Purpose: To develop and evaluate an automated segmentation method for accurate quantification of abdominal adipose tissue (AAT) depots (superficial subcutaneous adipose tissue [SSAT], deep subcutaneous adipose tissue [DSAT], and visceral adipose tissue [VAT]) in neonates and young children. Materials and Methods: This was a secondary analysis of prospectively collected data, which used abdominal MRI data from Growing Up in Singapore Towards healthy Outcomes, or GUSTO, a longitudinal mother–offspring cohort, to train and evaluate a convolutional neural network for volumetric AAT segmentation. The data comprised imaging volumes of 333 neonates obtained at early infancy (age 2 weeks, 180 male neonates) and 755 children aged either 4.5 years (n = 316, 150 male children) or 6 years (n = 439, 219 male children). The network was trained on images of 761 randomly selected volumes (neonates and children combined) and evaluated on 100 neonatal volumes and 227 child volumes by using 10-fold validation. Automated segmentations were compared with expert-generated manual segmentation. Segmentation performance was assessed using Dice scores. Results: When the model was tested on the test datasets across the 10 folds, the model had strong agreement with the ground truth for all testing sets, with mean Dice similarity scores for SSAT, DSAT, and VAT, respectively, of 0.960, 0.909, and 0.872 in neonates and 0.944, 0.851, and 0.960 in children. The model generalized well to different body sizes and ages and to all abdominal levels. Conclusion: The proposed segmentation approach provided accurate automated volumetric assessment of AAT compartments on MR images of neonates and children.
URI: https://hdl.handle.net/10356/164171
ISSN: 2638-6100
DOI: 10.1148/ryai.2021200304
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
Organisations: Duke–National University of Singapore Medical School
KK Women’s and Children’s Hospital
Rights: © 2021 RSNA. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:LKCMedicine Journal Articles

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