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Purpose: 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. Methods: 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. Results: 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). Conclusions: 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 ∼1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study. [ABSTRACT FROM AUTHOR]

Overweight and obesity are highly prevalent conditions worldwide, linked to an increased risk for death, disability and disease due to metabolic and biochemical abnormalities affecting the biological human system throughout different domains. Biomarkers, defined as indicators of biological processes in health and disease, relevant for body mass excess management have been identified according to different criteria, including anthropometric and molecular indexes, as well as physiological and behavioural aspects. Analysing these different biomarkers, we identified their potential role in diagnosis, prognosis and treatment. Epigenetic biomarkers, cellular mediators of inflammation and factors related to microbiota-host interactions may be considered to have a theranostic value. Though, the molecular processes responsible for the biological phenomenology detected by the other analysed markers, is not clear yet. Nevertheless, these biomarkers possess valuable diagnostic and prognostic power. A new frontier for theranostic biomarkers can be foreseen in the exploitation of parameters defining behaviours and lifestyles linked to the risk of obesity, capable to describe the effects of interventions for obesity prevention and treatment which include also behaviour change strategies.