Assessment of skeletal muscle using deep learning on low-dose CT images
The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. T...
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| Published in | Global Health & Medicine Vol. 5; no. 5; pp. 278 - 284 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
National Center for Global Health and Medicine
31.10.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2434-9186 2434-9194 2434-9194 |
| DOI | 10.35772/ghm.2023.01050 |
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| Abstract | The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. The purpose of this study was to assess the performance of deep learning models (DLMs) for skeletal muscle mass measurement using low-dose abdominal CT. The primary dataset used in this study included 11,494 low-dose abdominal CT images at navel level acquired in 7,370 subjects for metabolic syndrome screening. The publicly available Cancer Imaging Archive (TCIA) dataset, including 5,801 abdominal CT images, was used as a complementary dataset. For abdominal CT image segmentation, we used the SegU-net DLM with different filter size and hierarchical depth. The segmentation accuracy was assessed by measuring the dice similarity coefficient (DSC), cross-sectional area (CSA) error, and Bland-Altman plots. The proposed DLM achieved a DSC of 0.992 ± 0.012, a CSA error of 0.41 ± 1.89%, and a Bland-Altman percent difference of -0.1 ± 3.8%. The proposed DLM was able to automatically segment skeletal muscle mass measurements from low-dose abdominal CT with high accuracy. |
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| AbstractList | The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. The purpose of this study was to assess the performance of deep learning models (DLMs) for skeletal muscle mass measurement using low-dose abdominal CT. The primary dataset used in this study included 11,494 low-dose abdominal CT images at navel level acquired in 7,370 subjects for metabolic syndrome screening. The publicly available Cancer Imaging Archive (TCIA) dataset, including 5,801 abdominal CT images, was used as a complementary dataset. For abdominal CT image segmentation, we used the SegU-net DLM with different filter size and hierarchical depth. The segmentation accuracy was assessed by measuring the dice similarity coefficient (DSC), cross-sectional area (CSA) error, and Bland-Altman plots. The proposed DLM achieved a DSC of 0.992 ± 0.012, a CSA error of 0.41 ± 1.89%, and a Bland-Altman percent difference of -0.1 ± 3.8%. The proposed DLM was able to automatically segment skeletal muscle mass measurements from low-dose abdominal CT with high accuracy. The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. The purpose of this study was to assess the performance of deep learning models (DLMs) for skeletal muscle mass measurement using low-dose abdominal CT. The primary dataset used in this study included 11,494 low-dose abdominal CT images at navel level acquired in 7,370 subjects for metabolic syndrome screening. The publicly available Cancer Imaging Archive (TCIA) dataset, including 5,801 abdominal CT images, was used as a complementary dataset. For abdominal CT image segmentation, we used the SegU-net DLM with different filter size and hierarchical depth. The segmentation accuracy was assessed by measuring the dice similarity coefficient (DSC), cross-sectional area (CSA) error, and Bland-Altman plots. The proposed DLM achieved a DSC of 0.992 ± 0.012, a CSA error of 0.41 ± 1.89%, and a Bland-Altman percent difference of -0.1 ± 3.8%. The proposed DLM was able to automatically segment skeletal muscle mass measurements from low-dose abdominal CT with high accuracy.The visceral fat area obtained by computed tomography (CT) at the navel level is clinically used as an indicator of visceral fat obesity in Japan. Analysis of skeletal muscle mass using CT images at the navel level may potentially support concurrent assessment of sarcopenia and sarcopenic obesity. The purpose of this study was to assess the performance of deep learning models (DLMs) for skeletal muscle mass measurement using low-dose abdominal CT. The primary dataset used in this study included 11,494 low-dose abdominal CT images at navel level acquired in 7,370 subjects for metabolic syndrome screening. The publicly available Cancer Imaging Archive (TCIA) dataset, including 5,801 abdominal CT images, was used as a complementary dataset. For abdominal CT image segmentation, we used the SegU-net DLM with different filter size and hierarchical depth. The segmentation accuracy was assessed by measuring the dice similarity coefficient (DSC), cross-sectional area (CSA) error, and Bland-Altman plots. The proposed DLM achieved a DSC of 0.992 ± 0.012, a CSA error of 0.41 ± 1.89%, and a Bland-Altman percent difference of -0.1 ± 3.8%. The proposed DLM was able to automatically segment skeletal muscle mass measurements from low-dose abdominal CT with high accuracy. |
| ArticleNumber | 2023.01050 |
| Author | Matsushita, Yumi Noguchi, Tomoyuki Nakagawa, Toru Yokoyama, Tetsuji |
| Author_xml | – sequence: 1 fullname: Matsushita, Yumi organization: Department of Clinical Research, National Center for Global Health and Medicine, Tokyo, Japan – sequence: 1 fullname: Nakagawa, Toru organization: Hitachi, Ltd. Hitachi Health Care Center, Ibaraki, Japan – sequence: 1 fullname: Noguchi, Tomoyuki organization: Department of Radiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan – sequence: 1 fullname: Yokoyama, Tetsuji organization: Department of Health Promotion, National Institute of Public Health, Saitama, Japan |
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| Cites_doi | 10.1016/j.orcp.2012.02.003 10.1093/ajcn/50.5.1231 10.1038/s41598-021-00161-5 10.1007/s00520-021-06603-0 10.1016/j.jamda.2019.12.012 10.1139/H08-075 10.1007/s40618-013-0011-3 10.1097/01.rct.0000228164.08968.e8 10.1016/j.ejca.2015.12.030 10.1016/j.archger.2011.06.015 10.1159/000499607 10.1038/s41598-018-29825-5 10.1093/mnras/stab1518 10.1371/journal.pone.0086902 10.1007/s00421-010-1473-z 10.1002/jcsm.12573 10.1007/s10278-013-9622-7 10.3389/fonc.2021.580806 10.1109/TIP.2021.3120053 10.1253/circj.66.987 10.1152/japplphysiol.00744.2004 10.1007/s00405-022-07545-x 10.2214/AJR.21.26486 10.1093/annonc/mdp605 10.1016/j.acra.2019.03.011 10.1007/s10916-020-1541-9 10.3390/nu12030755 10.1259/bjr.20190327 10.1148/radiol.2018181432 10.3348/kjr.2019.0470 10.1007/s00261-020-02755-5 10.1109/TPAMI.2016.2644615 |
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| Copyright | 2023 National Center for Global Health and Medicine 2023, National Center for Global Health and Medicine. 2023, National Center for Global Health and Medicine 2023 |
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Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf (accessed September 1, 2023) 26. Burns JE, Yao J, Chalhoub D, Chen JJ, Summers RM. A machine learning algorithm to estimate sarcopenia on abdominal CT. Acad Radiol. 2020; 27:311-320. 20. Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, Huh J, Lee TY, Park TY, Lee J, Kim KW. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol. 2020; 21:88-100. 8. Sanada K, Miyachi M, Tanimoto M, Yamamoto K, Murakami H, Okumura S, Gando Y, Suzuki K, Tabata I, Higuchi M. A cross-sectional study of sarcopenia in Japanese men and women: Reference values and association with cardiovascular risk factors. Eur J Appl Physiol. 2010; 110:57-65. 32. Chen LK, Woo J, Assantachai P, et al. 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A deep learning-based model that reduces speed of sound aberrations for improved in vivo photoacoustic imaging. IEEE Trans Image Process. 2021; 30:8773-8784. 5. Baek SJ, Nam GE, Han KD, Choi SW, Jung SW, Bok AR, Kim YH, Lee KS, Han BD, Kim DH . Sarcopenia and sarcopenic obesity and their association with dyslipidemia in Korean elderly men: The 2008-2010 Korea National Health and Nutrition Examination Survey. J Endocrinol Invest. 2014; 37:247-260. 31. Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017; 39:2481-2495. 14. Zhao B, Colville J, Kalaigian J, Curran S, Jiang L, Kijewski P, Schwartz LH. Automated quantification of body fat distribution on volumetric computed tomography. J Comput Assist Tomogr. 2006; 30:777-783. 33. Ministry of the Environment Government of Japan. BOOKLET to Provide Basic Information Regarding Health Effects of Radiation 2nd edition: Chapter 3 Health Effects of Radiation: Risks of Health Effects of Radiation. https://www.env.go.jp/en/chemi/rhm/basic-info/1st/03.html (accessed September 1, 2023) 12. Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018; 8:11369. 3. Antoun S, Baracos VE, Birdsell L, Escudier B, Sawyer MB. Low body mass index and sarcopenia associated with dose-limiting toxicity of sorafenib in patients with renal cell carcinoma. Ann Oncol. 2010; 21:1594-1598. 11. Walowski CO, Braun W, Maisch MJ, Jensen B, Peine S, Norman K, Müller MJ, Westphal AB. Reference values for skeletal muscle mass - Current concepts and methodological considerations. Nutrients. 2020; 12:755. 21. Perez AA, Pickhardt PJ, Elton DC, Sandfort V, Summers RM. Fully automated CT imaging biomarkers of bone, muscle, and fat: Correcting for the effect of intravenous contrast. Abdom Radiol (NY). 2021; 46:1229-1235. 27. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging. 2013; 26:1045-1057. 2. Shachar SS, Williams GR, Muss HB, Nishijima TF. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur J Cancer. 2016; 57:58-67. 6. Han K, Park YM, Kwon HS, Ko SH, Lee SH, Yim HW, Lee WC, Park YG, Kim MK, Park YM. Sarcopenia as a determinant of blood pressure in older Koreans: findings from the Korea National Health and Nutrition Examination Surveys (KNHANES) 2008-2010. PLoS One. 2014; 9:e86902. 25. Graffy PM, Liu J, Pickhardt PJ, Burns JE, Yao J, Summers RM. Deep learning-based muscle segmentation and quantification at abdominal CT: Application to a longitudinal adult screening cohort for sarcopenia assessment. Br J Radiol. 2019; 92:20190327. 19. Ha J, Park T, Kim HK, Shin Y, Ko Y, Kim DW, Sung YS, Lee J, Ham SJ, Khang S, Jeong H, Koo K, Lee J, Kim KW. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep. 2021; 11:21656. 24. Bridge CP, Rosenthal M, Wright B, et al. Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks. https://link.springer.com/chapter/10.1007/978-3-030-01201-4_22#citeas (accessed September 1, 2023) 4. Raynard B, Pigneur F, Di Palma M, Deluche E, Goldwasser F. The prevalence of CT-defined low skeletal muscle mass in patients with metastatic cancer: a cross-sectional multicenter French study (the SCAN study). Support Care Cancer. 2022; 30:3119-3129. 10. Jensen B, Braun W, Geisler C, Both M, Klückmann K, Müller MJ, Westphal AB. Limitations of fat-free mass for the assessment of muscle mass in obesity. Obes Facts. 2019; 12:307-315. 35. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008; 33:997-1006. 22. Pickhardt PJ, Perez AA, Garrett JW, Graffy PM, Zea R, Summers RM. Fully automated deep learning tool for sarcopenia assessment on CT: L1 versus L3 vertebral level muscle measurements for opportunistic prediction of adverse clinical outcomes. AJR Am J Roentgenol. 2022; 218:124-131. 22 23 24 25 26 27 28 29 30 31 10 32 11 33 12 34 13 35 14 36 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – reference: 5. Baek SJ, Nam GE, Han KD, Choi SW, Jung SW, Bok AR, Kim YH, Lee KS, Han BD, Kim DH . Sarcopenia and sarcopenic obesity and their association with dyslipidemia in Korean elderly men: The 2008-2010 Korea National Health and Nutrition Examination Survey. J Endocrinol Invest. 2014; 37:247-260. – reference: 21. Perez AA, Pickhardt PJ, Elton DC, Sandfort V, Summers RM. Fully automated CT imaging biomarkers of bone, muscle, and fat: Correcting for the effect of intravenous contrast. Abdom Radiol (NY). 2021; 46:1229-1235. – reference: 18. Cespedes Feliciano EM, Popuri K, Cobzas D, Baracos VE, Beg MF, Khan AD, Ma C, Chow V, Prado CM, Xiao J, Liu V, Chen WY, Meyerhardt J, Albers KB, Caan BJ. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. J Cachexia Sarcopenia Muscle. 2020; 11:1258-1269. – reference: 15. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf (accessed September 1, 2023) – reference: 30. Jeon S, Choi W, Park B, Kim C. A deep learning-based model that reduces speed of sound aberrations for improved in vivo photoacoustic imaging. IEEE Trans Image Process. 2021; 30:8773-8784. – reference: 6. Han K, Park YM, Kwon HS, Ko SH, Lee SH, Yim HW, Lee WC, Park YG, Kim MK, Park YM. Sarcopenia as a determinant of blood pressure in older Koreans: findings from the Korea National Health and Nutrition Examination Surveys (KNHANES) 2008-2010. PLoS One. 2014; 9:e86902. – reference: 32. Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020; 21:300-307.e2. – reference: 9. Tanimoto Y, Watanabe M, Sun W, Hirota C, Sugiura Y, Kono R, Saito M, Kono K. Association between muscle mass and disability in performing instrumental activities of daily living (IADL) in community-dwelling elderly in Japan. Archives of gerontology and geriatrics. 2012; 54:e230-e233. – reference: 23. Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, Sugimoto M, Takahashi N, Erickson BJ. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology. 2019; 290:669-679. – reference: 1. Rosenberg IH. Summary comments (Epidemiologic and methodologic problems in determining nutritional status of older persons). Am J Clin Nutr. 1989; 50:1231-1233. – reference: 29. Bianco M, Giri SK, Iliev IT, Mellema G. Deep learning approach for identification of H II regions during reionization in 21-cm observations. Monthly Notices of the Royal Astronomical Society. 2021; 505:3982-3997. – reference: 26. Burns JE, Yao J, Chalhoub D, Chen JJ, Summers RM. A machine learning algorithm to estimate sarcopenia on abdominal CT. Acad Radiol. 2020; 27:311-320. – reference: 24. Bridge CP, Rosenthal M, Wright B, et al. Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks. https://link.springer.com/chapter/10.1007/978-3-030-01201-4_22#citeas (accessed September 1, 2023) – reference: 35. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008; 33:997-1006. – reference: 36. Vangelov B, Bauer J, Moses D, Smee R. A prediction model for skeletal muscle evaluation and computed tomography-defined sarcopenia diagnosis in a predominantly overweight cohort of patients with head and neck cancer. Eur Arch Otorhinolaryngol. 2023; 280:321-328. – reference: 34. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J, Heymsfield SB, Heshka S. Total body skeletal muscle and adipose tissue volumes: Estimation from a single abdominal cross-sectional image. J Appl Physiol (1985). 2004; 97:2333-2338. – reference: 33. Ministry of the Environment Government of Japan. BOOKLET to Provide Basic Information Regarding Health Effects of Radiation 2nd edition: Chapter 3 Health Effects of Radiation: Risks of Health Effects of Radiation. https://www.env.go.jp/en/chemi/rhm/basic-info/1st/03.html (accessed September 1, 2023) – reference: 2. Shachar SS, Williams GR, Muss HB, Nishijima TF. Prognostic value of sarcopenia in adults with solid tumours: A meta-analysis and systematic review. Eur J Cancer. 2016; 57:58-67. – reference: 11. Walowski CO, Braun W, Maisch MJ, Jensen B, Peine S, Norman K, Müller MJ, Westphal AB. Reference values for skeletal muscle mass - Current concepts and methodological considerations. Nutrients. 2020; 12:755. – reference: 31. Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017; 39:2481-2495. – reference: 12. Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018; 8:11369. – reference: 4. Raynard B, Pigneur F, Di Palma M, Deluche E, Goldwasser F. The prevalence of CT-defined low skeletal muscle mass in patients with metastatic cancer: a cross-sectional multicenter French study (the SCAN study). Support Care Cancer. 2022; 30:3119-3129. – reference: 27. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging. 2013; 26:1045-1057. – reference: 3. Antoun S, Baracos VE, Birdsell L, Escudier B, Sawyer MB. Low body mass index and sarcopenia associated with dose-limiting toxicity of sorafenib in patients with renal cell carcinoma. Ann Oncol. 2010; 21:1594-1598. – reference: 20. Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, Huh J, Lee TY, Park TY, Lee J, Kim KW. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol. 2020; 21:88-100. – reference: 19. Ha J, Park T, Kim HK, Shin Y, Ko Y, Kim DW, Sung YS, Lee J, Ham SJ, Khang S, Jeong H, Koo K, Lee J, Kim KW. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci Rep. 2021; 11:21656. – reference: 10. Jensen B, Braun W, Geisler C, Both M, Klückmann K, Müller MJ, Westphal AB. Limitations of fat-free mass for the assessment of muscle mass in obesity. Obes Facts. 2019; 12:307-315. – reference: 13. 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