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CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies

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A Correction to this article was published on 04 September 2021

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Abstract

Background

There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and stroke to also impact upon the muscle to give rise to sarcopenic obesity. Phenotypic knowledge of obesity is crucial for profiling and management of obesity, as different fat—subcutaneous adipose tissue depots (SAT) and visceral adipose tissue depots (VAT) have various degrees of influence on metabolic syndrome and morbidities. Manual segmentation is time consuming and laborious. Study focuses on the development of a deep learning-based, complete data processing pipeline for MRI-based fat analysis, for large cohort studies which include (1) data augmentation and preprocessing (2) model zoo (3) visualization dashboard, and (4) correction tool, for automated quantification of fat compartments SAT and VAT.

Methods

Our sample comprised 190 healthy community-dwelling older adults from the Geri-LABS study with mean age of 67.85 ± 7.90 years, BMI 23.75 ± 3.65 kg/m2, 132 (69.5%) female, and mainly Chinese ethnicity. 3D-modified Dixon T1-weighted gradient-echo MR images were acquired. Residual global aggregation-based 3D U-Net (RGA-U-Net) and standard 3D U-Net were trained to segment SAT, VAT, superficial and deep subcutaneous adipose tissue depots (SSAT and DSAT). Manual segmentation from 26 subjects was used as ground truth during training. Data augmentations, random bias, noise and ghosting were carried out to increase the number of training datasets to 130. Segmentation accuracy was evaluated using Dice and Hausdorff metrics.

Results

The accuracy of segmentation was SSAT:0.92, DSAT:0.88 and VAT:0.9. Average Hausdorff distance was less than 5 mm. Automated segmentation significantly correlated R2 > 0.99 (p < 0.001) with ground truth for all 3-fat compartments. Predicted volumes were within ± 1.96SD from Bland–Altman analysis.

Conclusions

DL-based, comprehensive SSAT, DSAT, and VAT analysis tool showed high accuracy and reproducibility and provided a comprehensive fat compartment composition analysis and visualization in less than 10 s.

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Availability of data and material

Data cannot be shared due to National policy.

Code availability

Can be made available based on the approval from the funding institute.

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Acknowledgements

Our sincere thanks to Singapore Bioimaging Consortium, A*STAR, and Tan Tock Seng Hospital for providing funds and data for conducting this study. We would also like to thank the support received from TechSource Systems Pte Ltd, especially Application Engineer Kevin Chng Jun Yan for his technical guidance and help rendered when needed during the development of the correction tool.

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Author notes

  1. K. N.Bhanu Prakash and C. S. Arvind have to be considered as joint first authors for their contribution to this work.

Authors and Affiliations

  1. Signal and Image Processing Group, Institute of Bioengineering and Bioimaging, 02-02, Helios,11, Biopolis Way, Singapore, 138667, Singapore

    Prakash KN Bhanu, Channarayapatna Srinivas Arvind & Ling Yun Yeow

  2. Department of Diagnostic Radiology, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore, 308433, Singapore

    Wen Xiang Chen & Cher Heng Tan

  3. Department of Geriatric Medicine, Tan Tock Seng Hospital, 11 Jln Tan Tock Seng, Singapore, 308433, Singapore

    Wee Shiong Lim

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  1. Prakash KN Bhanu
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Contributions

KNB, CS worked on deep learning-based framework development, data processing, concept development, correction tool, and manuscript. LY developed a correction tool and assisted in the manuscript. CHT, WSL, and WC collected data (image acquisition), generated ground truth, and worked on the manuscript.

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Correspondence to Prakash KN Bhanu.

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Bhanu, P.K., Arvind, C.S., Yeow, L.Y. et al. CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies. Magn Reson Mater Phy 35, 205–220 (2022). https://doi.org/10.1007/s10334-021-00946-9

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