Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-02-011 ISMRM Abstract

Deep Learning MRI Segmentation for Automated Quantification of Leg Tissue Volumes and Fluid Distribution in Lymphedema

Primary: Analysis Methods - Segmentation and Detection
Secondary: Analysis Methods - Classification and Prediction
368-02-011 · Segmentation for Musculoskeletal MRI Applications · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row I
Keywords: Segmentation Lymphedema Deep-learning MRI diagnostics Edema
Accepted
Zola Bzdek 1, Nick Tustison2, Rachelle Crescenzi2,3,4,5, Klaus Hagspiel2, Sheau-Chiann Chen6, Shannon Taylor3,4,5, Vanessa Crain3,5,7, Allison O'Brien Scott2,3,7,8
1Physics, University of Virginia, Charlottesville, United States of America
2Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, United States of America
3Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, United States of America
4Vanderbilt University, Nashville, United States of America
5Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, United States of America
6Department of Biostatistics, Vanderbilt University Medical Center, Nashville, United States of America
7University of Virginia, Charlottesville, United States of America
8Vanderbilt University Institute of Imaging Science, Nashville, United States of America
Presenting Author: Zola Bzdek

Synopsis

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References

1. Rockson SG. Advances in Lymphedema. Circ Res. 2021;128(12):2003-2016. doi:10.1161/CIRCRESAHA.121.318307 [doi]
2. Liu M, Zhang Y, Li X, Hao Q, Li B, Wang R. MRI-based volume measurement methods for staging primary lower extremity lymphedema: a single-center study of asymmetric volume difference-a diagnostic study. BMC Musculoskelet Disord. 2023;24(1):810. Published 2023 Oct 12. doi:10.1186/s12891-023-06912-x [doi]
3. Dayan JH, Wiser I, Verma R, et al. Regional Patterns of Fluid and Fat Accumulation in Patients with Lower Extremity Lymphedema Using Magnetic Resonance Angiography. Plast Reconstr Surg. 2020;145(2):555-563. doi:10.1097/PRS.0000000000006520 [doi]
4. Trinh L, Peterson P, Brorson H, Månsson S. Assessment of Subfascial Muscle/Water and Fat Accumulation in Lymphedema Patients Using Magnetic Resonance Imaging. Lymphat Res Biol. 2019;17(3):340-346. doi:10.1089/lrb.2018.0027 [doi]
5. Watanabe Y, Koshiyama M, Seki K, et al. Development and Themes of Diagnostic and Treatment Procedures for Secondary Leg Lymphedema in Patients with Gynecologic Cancers. Healthcare (Basel). 2019;7(3):101. Published 2019 Aug 27. doi:10.3390/healthcare7030101 [doi]
6. Taylor SL, Donahue PMC, Pridmore MD, et al. Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study. J Med Imaging (Bellingham). 2023;10(3):036001. doi:10.1117/1.JMI.10.3.036001 [doi]
7. Tustison NJ, Cook PA, Holbrook AJ, et al. The ANTsX ecosystem for quantitative biological and medical imaging. Sci Rep. 2021;11(1):9068. Published 2021 Apr 27. doi:10.1038/s41598-021-87564-6 [doi]
8. Crescenzi R, Donahue PMC, Garza M, et al. Subcutaneous Adipose Tissue Edema in Lipedema Revealed by Noninvasive 3T MR Lymphangiography. J Magn Reson Imaging. 2023;57(2):598-608. doi:10.1002/jmri.28281 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-02-011