Comprehensive End-to-End Workflow for Visceral Adipose Tissue and Subcutaneous Adipose Tissue quantification: Use Case to improve MRI accessibility

Abstract

Studies prove the correlation between Body Fat distribution and insulin resistance which is a major risk factor for Type 2 diabetes and cardiovascular diseases (CVD). Educating individuals with more accurate measures of fat distribution outside fat percentage and body mass index (BMI) along with preventive solutions to susceptible conditions encourage better lifestyle choices and routines. Essential fat compartments that constitute the total fat distribution are visceral adipose tissue (VAT), superficial subcutaneous adipose tissue (SSAT) and deep subcutaneous adipose tissue (DSAT) which can be measured using whole-body MRI from head to foot. We propose a two-stage solution: rapid Dixon sequence acquisition, fat compartment segmentation. A clinically standard and predefined protocol is designed to automate the acquisition with optimal pulse sequence parameters to satisfy any time constraint. Two separate fully convolutional networks (UNet) with attention gates trained on our in-house dataset of 53 patients are used to segment VAT and SAT respectively. Further, the SAT segment is sub-classified into SSAT and DSAT by detecting the fascia superficialis using modified level sets. The models are capable of segmenting VAT, DSAT, and SSAT from head to foot without any manual intervention. Our method achieves a dice score of 0.868 for SAT segmentation and 0.9107 for VAT segmentation. The whole pipeline from data acquisition to reporting can be completed in under 20 minutes. Furthermore, our experiments show that our approach to estimating the segmentations are better than similar deep learning models trained on abdomen MRI. Our study demonstrates a use case of how MRI as a modality can be used outside of a typical clinical setting and set up as an upstream imaging solution to make it a more accessible tool for health evaluation/screening for the public.