A fully convolutional neural network for comprehensive compartmentalization of abdominal adipose tissue compartments in MRI.

BACKGROUND: Existing literature has highlighted structural, physiological, and pathological disparities among abdominal adipose tissue (AAT) sub-depots. Accurate separation and quantification of these sub-depots are crucial for advancing our understanding of obesity and its comorbidities. However, the absence of clear boundaries between the sub-depots in medical imaging data has challenged their separation, particularly for internal adipose tissue (IAT) sub-depots. To date, the quantification of AAT sub-depots remains challenging, marked by a time-consuming, costly, and complex process. PURPOSE: To implement and evaluate a convolutional neural network to enable granular assessment of AAT by compartmentalization of subcutaneous adipose tissue (SAT) into superficial subcutaneous (SSAT) and deep subcutaneous (DSAT) adipose tissue, and IAT into intraperitoneal (IPAT), retroperitoneal (RPAT), and paraspinal (PSAT) adipose tissue. MATERIAL AND METHODS: MRI datasets were retrospectively collected from Singapore Preconception Study for Long-Term Maternal and Child Outcomes (S-PRESTO: 389 women aged 31.4 ± 3.9 years) and Singapore Adult Metabolism Study (SAMS: 50 men aged 28.7 ± 5.7 years). For all datasets, ground truth segmentation masks were created through manual segmentation. A Res-Net based 3D-UNet was trained and evaluated via 5-fold cross-validation on S-PRESTO data (N = 300). The model's final performance was assessed on a hold-out (N = 89) and an external test set (N = 50, SAMS). RESULTS: The proposed method enabled reliable segmentation of individual AAT sub-depots in 3D MRI volumes with high mean Dice similarity scores of 98.3%, 97.2%, 96.5%, 96.3%, and 95.9% for SSAT, DSAT, IPAT, RPAT, and PSAT respectively. CONCLUSION: Convolutional neural networks can accurately sub-divide abdominal SAT into SSAT and DSAT, and abdominal IAT into IPAT, RPAT, and PSAT with high accuracy. The presented method has the potential to significantly contribute to advancements in the field of obesity imaging and precision medicine.

Associations of maternal periconceptional alcohol consumption with offspring prehypertension/hypertension at age 6 years: the Growing Up in Singapore Towards healthy Outcomes prospective mother-offspring cohort study.

OBJECTIVE: To evaluate the relationship of the levels of maternal alcohol consumption during the 1 year before pregnancy recognition with childhood cardiorenal, metabolic, and neurocognitive health. METHODS: In 1106 women and their children from the Growing Up in Singapore Towards healthy Outcomes mother-offspring cohort, quantity of maternal alcohol consumption in the 12 months prior to pregnancy recognition was categorized as high (≥75th percentile: 1.9 g/day), low (<1.9 g/day), and none, and frequency of alcohol consumption was categorized as high (≥2-3 times/week), low (<2-3 times/week), and none. Offspring MRI-based abdominal fat depot, kidney, and brain volumes, blood pressure, metabolic syndrome score, and cognitive intelligence scores were assessed. Child prehypertension/hypertension at age 6 years was defined using a simplified pediatric threshold of 110/70 mmHg. RESULTS: The average maternal alcohol consumption in the year prior to pregnancy recognition was 2.5 g/day, which is lower than the daily maximal limit of one standard drink (10 g) recommended for women by Singapore's Ministry of Health. After adjusting for participant characteristics, alcohol consumption at least 1.9 g/day was associated with over two-fold higher risk (risk ratio = 2.18, P = 0.013) of child prehypertension and 15% greater kidney growth between early infancy and age 6 years (P = 0.040) compared with abstinence. Alcohol consumption was not associated with metabolic and neurocognitive health at age 6-7 years. The associations with high frequency of alcohol consumption were concordant with those obtained for quantity of alcohol consumption. CONCLUSION: Maternal self-reported alcohol consumption at least 1.9 g/day prior to pregnancy recognition was associated with increased risk of child prehypertension and rapid kidney growth. Our findings highlight the potential detrimental effects of low periconceptional alcohol consumption, below national guidelines on offspring cardiorenal health.

Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children.

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.Keywords Pediatrics, Deep Learning, Convolutional Neural Networks, Water-Fat MRI, Image Segmentation, Deep and Superficial Subcutaneous Adipose Tissue, Visceral Adipose TissueClinical trial registration no. NCT01174875 Supplemental material is available for this article. © RSNA, 2021.