Abstract
Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans. FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort. The CDFNet demonstrates increased accuracy and robustness compared to traditional deep learning networks. FatSegNet Dice score outperforms manual raters on VAT (0.850 vs. 0.788) and produces comparable results on SAT (0.975 vs. 0.982). The pipeline has excellent agreement for both test-retest (ICC VAT 0.998 and SAT 0.996) and manual re-editing (ICC VAT 0.999 and SAT 0.999). FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼1minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.
Highlights
ConclusionFatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study, and permits localized analysis of fat compartments
The excess of body fat depots is an increasing major public health issue worldwide and an important risk factor for the development of metabolic disorders and reduced quality of life [1, 2]
Langer et al [18] proposed a three channel UNet for Abdominal adipose tissue (AAT) segmentation, which is a conventional architecture for 2D medical image segmentation [19]. While this method showed promising results, we demonstrate that our network architecture outperforms the traditional UNet for segmenting AAT on our images with a wide range of anatomical variation
Summary
FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study, and permits localized analysis of fat compartments. It can reliably analyze a 3D Dixon MRI in ∼ 1 min, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study. KEYWORDS Subcutaneous adipose tissue, Visceral adipose tissue, Dixon MRI, Neural networks, Deep learning, Semantic segmentation
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