Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle
This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification. The data were split into training and test sets, with manual segmentation as the ground truth. Subcutaneous and visceral adipose tissues (SAT and VAT) were segmented separately. Muscle area, mean attenuation value, and intermuscular adipose tissue percentage (IMAT%) were calculated in the paravertebral muscle segmentation. The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. The AI achieved Dice scores above 0.87 and showed excellent correlations for VAT/SAT ratios, muscle attenuation value, and IMAT% (correlation coefficients > 0.98, p < 0.001). The mean differences between the AI and ground truth were minimal (VAT/SAT ratio: 0.7%; muscle attenuation value: 1 HU; IMAT%: <1%). In conclusion, we developed a feasible AI algorithm for automated 3D segmentation of adipose tissue and paravertebral muscle on chest CT.
Authors
- Xinan Wang (ORCID: https://orcid.org/0000-0001-5236-1105)
- N Sugihara (ORCID: https://orcid.org/0009-0004-0229-4986)
- Gary Matthew Hunninghake (ORCID: https://orcid.org/0000-0001-9782-2841)
- Minoru Nakatsugawa (ORCID: https://orcid.org/0000-0002-4561-0641)
- Mark L. Schiebler (ORCID: https://orcid.org/0000-0002-9120-5428)
- Vladimir I. Valtchinov (ORCID: https://orcid.org/0000-0002-3272-4826)
- Jiyeon Song (ORCID: https://orcid.org/0000-0002-2180-3625)
- Hiroto Hatabu (ORCID: https://orcid.org/0000-0003-1436-2988)
- Mizuki Nishino (ORCID: https://orcid.org/0000-0003-0275-0502)
- Takuya Hino (ORCID: https://orcid.org/0000-0002-8225-0737)
- Noriaki Wada (ORCID: https://orcid.org/0009-0001-1727-293X)
- Akihiro Koga (ORCID: https://orcid.org/0000-0003-1546-1152)
- Akinori Hata (ORCID: https://orcid.org/0000-0003-1149-7094)
- Yi Li (ORCID: https://orcid.org/0000-0003-1720-2760)
- Kota Aoyagi (ORCID: https://orcid.org/0000-0002-7769-4958)
- Masahiro Ozaki (ORCID: https://orcid.org/0009-0008-1500-5468)
- Yohei Muraguchi (ORCID: https://orcid.org/0009-0001-9325-2981)
- Masami Kawagishi (ORCID: https://orcid.org/0009-0006-8850-2372)
- David C. Christiani
- Takuo Negishi
- Noriyuki Tomiyama
Institutions
- Brigham and Women's Hospital (US)
- Canon (Japan) (JP)
- University of Wisconsin–Madison (US)
- Kyushu University (JP)
- Harvard University (US)
- University of Michigan (US)
- Dana-Farber Cancer Institute (US)
- Canon Medical Systems Corporation (Japan) (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-12-30
- DOI
- https://doi.org/10.1038/s41598-024-83897-0
- Citations
- 12
- Primary Topic
- Body Composition Measurement Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.92
Funders
- Brigham and Women's Hospital
- Canon Medical Systems Corporation
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Cancer Institute