Automated Abdominal Adipose Tissue Segmentation and Volume Quantification on Longitudinal MRI using 3D Convolutional Neural Networks with Multi-Contrast Inputs

Objective: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.

Materials and Methods: 920 adults with overweight/obesity were scanned twice at multiple 3T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n=646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).

Results: ACD 3D U-Net achieved rapid (<4.8 sec/subject) segmentation with high DICE-SAT (median≥0.994) and DICE-VAT (median≥0.976), small FN (median ≤0.7%), and FP (median≤1.1%). 3D nnU-Net yielded rapid (<2.5 sec/subject) segmentation with similar DICE-SAT (median≥0.992), DICE-VAT (median≥0.979), FN (median ≤1.1%) and FP (median ≤1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC>0.997) in longitudinal analysis.

Discussion: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.