565-05-013 ISMRM Abstract

3D nnU-Net-Based Automated Segmentation of Abdominal Adipose Tissue in Children using Free-Breathing Dixon MRI

Primary: Pediatrics - Body

Secondary: Analysis Methods - Segmentation and Detection

565-05-013 · Segmentation for Body Applications · Wednesday, 13 May, 4:00 PM–4:55 PM · Digital Posters Row F

Keywords: Pediatric Deep learning Image segmentation Adipose tissue Dixon MRI

Accepted

Wenwen Zhang¹, Sevgi Gokce Kafali^2,3, Shu-Fu Shih⁴, Timothy Adamos⁵, Kelsey Kuwahara⁶, Ashley Dong⁵, Jessica li⁵, Timoteo I Delgado^1,4, Shahnaz Ghahremani⁴, Kara Calkins⁵, Holden H Wu ^1,4

¹Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine at UCLA, Los Angeles, United States of America

²Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital, Munich, Germany

³Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, United States of America

⁴Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, United States of America

⁵Department of Pediatrics, David Geffen School of Medicine at UCLA, Los Angeles, United States of America

⁶Department of Cognitive Science, David Geffen School of Medicine at UCLA, Los Angeles, United States of America

Presenting Author: Holden H Wu

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Shuster A, Patlas M, Pinthus JH, Mourtzakis M. The clinical importance of visceral adiposity: a critical review of methods for visceral adipose tissue analysis. Br J Radiol. 2012 Jan;85(1009):1–10.

2. Somasundaram A, Wu M, Reik A, Rupp S, Han J, Naebauer S, et al. Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation. Radiol Artif Intell. 2024 Jul;6(4):e230471.

3. Estrada S, Lu R, Conjeti S, Orozco-Ruiz X, Panos-Willuhn J, Breteler MMB, et al. FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI. Magn Reson Med. 2020 Apr;83(4):1471–83.

4. Küstner T, Hepp T, Fischer M, Schwartz M, Fritsche A, Häring HU, et al. Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies. Radiol Artif Intell. 2020 Nov;2(6):e200010.

5. Wang Z, Cheng C, Peng H, Qi Y, Wan Q, Zhou H, et al. Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning. Magma N Y N. 2022 Apr;35(2):193–203.

6. Kafali SG, Shih SF, Li X, Kim GHJ, Kelly T, Chowdhury S, et al. Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs. Magn Reson Mater Phys Biol Med. 2024 Feb 1;37(3):491–506.

7. Tong Wu; Santiago Estrada; Renza van Gils; Ruisheng Su; Vincent W. V. Jaddoe; Edwin H. G. Oei; Stefan Klein. Automated Deep Learning–Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study [Internet]. [cited 2025 Oct 20]. Available from: https://ajronline.org/doi/epdf/10.2214/AJR.23.29570

8. Kway YM, Thirumurugan K, Tint MT, Michael N, Shek LPC, Yap FKP, Tan KH, Godfrey KM, Chong YS, Fortier MV, Marx UC, Eriksson JG, Lee YS, Velan SS, Feng M, Sadananthan SA. Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children [Internet]. [cited 2025 Oct 20]. Available from: https://pubs.rsna.org/doi/epdf/10.1148/ryai.2021200304

9. Ogunleye OA, Raviprakash H, Simmons AM, Bovell RTM, Martinez PE, Yanovski JA, et al. A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging. Tomography. 2023 Feb;9(1):139–49.

10. Armstrong T, Dregely I, Stemmer A, Han F, Natsuaki Y, Sung K, et al. Free-breathing liver fat quantification using a multiecho 3D stack-of-radial technique. Magn Reson Med. 2018;79(1):370–82.

11. Armstrong T, Ly KV, Murthy S, Ghahremani S, Kim GHJ, Calkins KL, et al. Free-breathing quantification of hepatic fat in healthy children and children with nonalcoholic fatty liver disease using a multi-echo 3-D stack-of-radial MRI technique. Pediatr Radiol. 2018 Jul 1;48(7):941–53.

12. Zhong X, Hu HH, Armstrong T, Li X, Lee YH, Tsao TC, et al. Free-Breathing Volumetric Liver and Proton Density Fat Fraction Quantification in Pediatric Patients Using Stack-of-Radial MRI With Self-Gating Motion Compensation. J Magn Reson Imaging. 2021;53(1):118–29.

13. Kafali SG, Adamos TR, Kuwahara K, Dong AM, Li J, Zhou W, Shih S-F, Delgado TI, Ghahremani S, Calkins KL, Wu HH. Automated Abdominal Adipose Tissue Segmentation and Quantification in Children using Free-Breathing Dixon MRI and 3D Neural Networks [ISMRM 2025]. [cited 2025 Sep 23]. 0540

14. Isensee F, Wald T, Ulrich C, Baumgartner M, Roy S, Maier-Hein K, et al. nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation [Internet]. arXiv; 2024 [cited 2024 Sep 9]. Available from: http://arxiv.org/abs/2404.09556

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/565-05-013