FatSegNet is a novel, fast, and fully automated deep learning pipeline 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. The proposed pipeline implements a three-stage design with our Competitive Dense Fully Convolutional Network (CDFNet) architecture at the core for localizing the abdominal region and segmenting the abdominal adipose tissue.

FatSegNet Pipeline

Fig 1. Proposed FatSegNet pipeline for segmenting Abdominal Adipose tissue

Our CDFNet is implemented by adopting a Dense-Unet architecture and extending it toward competitive learning via maxout activations. The Maxout operation computes the maximum at each spatial location across feature maps instead of concatenating them. Some benefits of competitive learning through Maxout activations are:


Fig 2. CDFNet architecture, the output filter for all convolutional layers in CUB, CDB and bottleneck were standardized to 64 channels.

For more information into competitive learning please check:

Proof of Concept

FatSegNet is tested and validated in the Rhineland Study – a large prospective cohort study based in Bonn, Germany. We evaluate the whole pipeline with respect to robustness and reliability against two independent test sets: a manually edited and a test-retest set. Additionally, we present a case study on unseen data comparing the VAT-V and SAT-V calculated from the FatSegNet segmentations against BMI to replicate age and sex effects on these volumes in a large cohort.

FatSegNet Performance

N Metric VAT SAT
Segmentation Accuracy: FatSegNet vs. Inter-Rater variability
5 DSC (SD) 0.850 (0.076) vs. 0.788 (.060) 0.975 (0.018) vs. 0.982 (0.018)
Test-Retest Reliability: Agreement between predictions of 2 consecutive scans
17 ICC (A,1)
[95% CI
[0.995 - 0.999]
[0.986 - 0.999]
Segmentation Generalizability: Agreement between FatSegNet and manually corrected predictions
50 ICC (A,1)
[95% CI
[0.994 - 0.999]
[0.999 – 1.000]

Validation in the Rhineland Study (N=587)

Replication of known age and sex effects on VAT and SAT volumes


Fig 3. Association between age and volumes of SAT and VAT in men and women. VAT volumes significantly increased with age, and SAT volumes weakly associated with age in women (β= 0.02, p= 0.01) [Machann et al.2005, Kuk et al.2005]

Performance on Unseen Data


Fig 4. Examples of FatSegNet results of different body shapes (blue: SAT, green: VAT, orange: bone and surrounding tissue, and red: other-tissue). A) arms in front of the abdominal cavity, B) obese-BMI, C) underweight-BMI, D) breast, and E) deviated spine

Tool and Paper


MRM Cover