Skip to main content
Erschienen in: Skeletal Radiology 3/2020

08.08.2019 | Scientific Article

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

verfasst von: Robert Hemke, Colleen G. Buckless, Andrew Tsao, Benjamin Wang, Martin Torriani

Erschienen in: Skeletal Radiology | Ausgabe 3/2020

Einloggen, um Zugang zu erhalten

Abstract

Objective

To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.

Materials and methods

We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.

Results

The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).

Conclusions

Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
Literatur
1.
Zurück zum Zitat Grimaldi A, Richardson C, Stanton W, Durbridge G, Donnelly W, Hides J. The association between degenerative hip joint pathology and size of the gluteus medius, gluteus minimus and piriformis muscles. Man Ther. 2009;14:605–10.CrossRef Grimaldi A, Richardson C, Stanton W, Durbridge G, Donnelly W, Hides J. The association between degenerative hip joint pathology and size of the gluteus medius, gluteus minimus and piriformis muscles. Man Ther. 2009;14:605–10.CrossRef
2.
Zurück zum Zitat ten Dam L, van der Kooi AJ, Rövekamp F, Linssen WHJP, de Visser M. Comparing clinical data and muscle imaging of DYSF and ANO5-related muscular dystrophies. Neuromuscul Disord. 2014;24:1097–102.CrossRef ten Dam L, van der Kooi AJ, Rövekamp F, Linssen WHJP, de Visser M. Comparing clinical data and muscle imaging of DYSF and ANO5-related muscular dystrophies. Neuromuscul Disord. 2014;24:1097–102.CrossRef
3.
Zurück zum Zitat Woodley SJ, Nicholson HD, Livingstone V, Doyle TC, Meikle GR, Macintosh JE, et al. Lateral hip pain: findings from magnetic resonance imaging and clinical examination. J Orthop Sports Phys Ther. 2008;38:313–28.CrossRef Woodley SJ, Nicholson HD, Livingstone V, Doyle TC, Meikle GR, Macintosh JE, et al. Lateral hip pain: findings from magnetic resonance imaging and clinical examination. J Orthop Sports Phys Ther. 2008;38:313–28.CrossRef
4.
Zurück zum Zitat Pfirrmann CWA, Notzli HP, Dora C, Hodler J, Zanetti M. Abductor tendons and muscles assessed at MR imaging after total hip arthroplasty in asymptomatic and symptomatic patients. Radiology. 2005;235:969–76.CrossRef Pfirrmann CWA, Notzli HP, Dora C, Hodler J, Zanetti M. Abductor tendons and muscles assessed at MR imaging after total hip arthroplasty in asymptomatic and symptomatic patients. Radiology. 2005;235:969–76.CrossRef
5.
Zurück zum Zitat Ikezoe T, Mori N, Nakamura M, Ichihashi N. Atrophy of the lower limbs in elderly women: is it related to walking ability? Eur J Appl Physiol. 2011;111:989–95.CrossRef Ikezoe T, Mori N, Nakamura M, Ichihashi N. Atrophy of the lower limbs in elderly women: is it related to walking ability? Eur J Appl Physiol. 2011;111:989–95.CrossRef
6.
Zurück zum Zitat Kiyoshige Y, Watanabe E. Fatty degeneration of gluteus minimus muscle as a predictor of falls. Arch Gerontol Geriatr. 2015;60:59–61.CrossRef Kiyoshige Y, Watanabe E. Fatty degeneration of gluteus minimus muscle as a predictor of falls. Arch Gerontol Geriatr. 2015;60:59–61.CrossRef
7.
Zurück zum Zitat Marcus RL, Addison O, Kidde JP, Dibble LE, Lastayo PC. Skeletal muscle fat infiltration: impact of age, inactivity, and exercise. J Nutr Health Aging. 2010;14:362–6.CrossRef Marcus RL, Addison O, Kidde JP, Dibble LE, Lastayo PC. Skeletal muscle fat infiltration: impact of age, inactivity, and exercise. J Nutr Health Aging. 2010;14:362–6.CrossRef
8.
Zurück zum Zitat Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci. 2005;60:324–33.CrossRef Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci. 2005;60:324–33.CrossRef
9.
Zurück zum Zitat Oliveira A, Vaz C. The role of sarcopenia in the risk of osteoporotic hip fracture. Clin Rheumatol. 2015;34:1673–80.CrossRef Oliveira A, Vaz C. The role of sarcopenia in the risk of osteoporotic hip fracture. Clin Rheumatol. 2015;34:1673–80.CrossRef
10.
Zurück zum Zitat Chang C-D, Wu JS, Mhuircheartaigh JN, Hochman MG, Rodriguez EK, Appleton PT, et al. Effect of sarcopenia on clinical and surgical outcome in elderly patients with proximal femur fractures. Skelet Radiol. 2018;47:771–7.CrossRef Chang C-D, Wu JS, Mhuircheartaigh JN, Hochman MG, Rodriguez EK, Appleton PT, et al. Effect of sarcopenia on clinical and surgical outcome in elderly patients with proximal femur fractures. Skelet Radiol. 2018;47:771–7.CrossRef
11.
Zurück zum Zitat Brown JC, Cespedes Feliciano EM, Caan BJ. The evolution of body composition in oncology-epidemiology, clinical trials, and the future of patient care: facts and numbers. J Cachexia Sarcopenia Muscle. 2018;9:1200–8.CrossRef Brown JC, Cespedes Feliciano EM, Caan BJ. The evolution of body composition in oncology-epidemiology, clinical trials, and the future of patient care: facts and numbers. J Cachexia Sarcopenia Muscle. 2018;9:1200–8.CrossRef
12.
Zurück zum Zitat Yoo T, Lo WD, Evans DC. Computed tomography measured psoas density predicts outcomes in trauma. Surgery. 2017;162:377–84.CrossRef Yoo T, Lo WD, Evans DC. Computed tomography measured psoas density predicts outcomes in trauma. Surgery. 2017;162:377–84.CrossRef
13.
Zurück zum Zitat Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50:889–96.CrossRef Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc. 2002;50:889–96.CrossRef
14.
Zurück zum Zitat Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology. 2019;290:669–79.CrossRef Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology. 2019;290:669–79.CrossRef
15.
Zurück zum Zitat Wang Y, Qiu Y, Thai T, Moore K, Liu H, Zheng B. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Prog Biomed. 2017;144:97–104.CrossRef Wang Y, Qiu Y, Thai T, Moore K, Liu H, Zheng B. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Prog Biomed. 2017;144:97–104.CrossRef
16.
Zurück zum Zitat Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, et al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging. 2017;30:487–98.CrossRef Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, et al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging. 2017;30:487–98.CrossRef
17.
Zurück zum Zitat Yang YX, Chong MS, Tay L, Yew S, Yeo A, Tan CH. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images. Magma (New York, NY). 2016;29:723–31. Yang YX, Chong MS, Tay L, Yew S, Yeo A, Tan CH. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images. Magma (New York, NY). 2016;29:723–31.
18.
Zurück zum Zitat Momose T, Inaba Y, Choe H, Kobayashi N, Tezuka T, Saito T. CT-based analysis of muscle volume and degeneration of gluteus medius in patients with unilateral hip osteoarthritis. BMC Musculoskelet Disord. 2017;18:457.CrossRef Momose T, Inaba Y, Choe H, Kobayashi N, Tezuka T, Saito T. CT-based analysis of muscle volume and degeneration of gluteus medius in patients with unilateral hip osteoarthritis. BMC Musculoskelet Disord. 2017;18:457.CrossRef
19.
Zurück zum Zitat Uemura K, Takao M, Sakai T, Nishii T, Sugano N. Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplast. 2016;31:906–912.e1.CrossRef Uemura K, Takao M, Sakai T, Nishii T, Sugano N. Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplast. 2016;31:906–912.e1.CrossRef
20.
Zurück zum Zitat Rutten IJG, van Dijk DPJ, Kruitwagen RFPM, Beets-Tan RGH, Olde Damink SWM, van Gorp T. Loss of skeletal muscle during neoadjuvant chemotherapy is related to decreased survival in ovarian cancer patients. J Cachexia Sarcopenia Muscle. 2016;7:458–66.CrossRef Rutten IJG, van Dijk DPJ, Kruitwagen RFPM, Beets-Tan RGH, Olde Damink SWM, van Gorp T. Loss of skeletal muscle during neoadjuvant chemotherapy is related to decreased survival in ovarian cancer patients. J Cachexia Sarcopenia Muscle. 2016;7:458–66.CrossRef
21.
Zurück zum Zitat Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical image computing and computer-assisted intervention: Springer; 2015. p. 234–41. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical image computing and computer-assisted intervention: Springer; 2015. p. 234–41.
22.
Zurück zum Zitat Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: a system for large-scale machine learning. OSDI. usenix.org. 2016. p. 265–283. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: a system for large-scale machine learning. OSDI. usenix.​org. 2016. p. 265–283.
23.
Zurück zum Zitat Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.CrossRef Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297–302.CrossRef
24.
Zurück zum Zitat Strulov Shachar S, 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.CrossRef Strulov Shachar S, 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.CrossRef
25.
Zurück zum Zitat Hopkins JJ, Sawyer MB. A review of body composition and pharmacokinetics in oncology. Expert Rev Clin Pharmacol. 2017;10:947–56.CrossRef Hopkins JJ, Sawyer MB. A review of body composition and pharmacokinetics in oncology. Expert Rev Clin Pharmacol. 2017;10:947–56.CrossRef
26.
Zurück zum Zitat Chung H, Cobzas D, Birdsell L, Lieffers J, Baracos V. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. Proc SPIE 7261, medical imaging 2009: visualization, image-guided procedures, and modeling, 72610K. 2009. Chung H, Cobzas D, Birdsell L, Lieffers J, Baracos V. Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis. Proc SPIE 7261, medical imaging 2009: visualization, image-guided procedures, and modeling, 72610K. 2009.
27.
Zurück zum Zitat Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016;35:512–20.CrossRef Popuri K, Cobzas D, Esfandiari N, Baracos V, Jägersand M. Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging. 2016;35:512–20.CrossRef
28.
Zurück zum Zitat Kim YJ, Lee SH, Kim TY, Park JY, Choi SH, Kim KG. Body fat assessment method using CT images with separation mask algorithm. J Digit Imaging. 2013;26:155–62.CrossRef Kim YJ, Lee SH, Kim TY, Park JY, Choi SH, Kim KG. Body fat assessment method using CT images with separation mask algorithm. J Digit Imaging. 2013;26:155–62.CrossRef
29.
Zurück zum Zitat Parikh AM, Coletta AM, Yu ZH, Rauch GM, Cheung JP, Court LE, et al. Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images. PLoS One. 2017 Aug 31;12(8):e0183515CrossRef Parikh AM, Coletta AM, Yu ZH, Rauch GM, Cheung JP, Court LE, et al. Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images. PLoS One. 2017 Aug 31;12(8):e0183515CrossRef
30.
Zurück zum Zitat Kullberg J, Hedström A, Brandberg J, Strand R, Johansson L, Bergström G, et al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep. 2017;7:10425.CrossRef Kullberg J, Hedström A, Brandberg J, Strand R, Johansson L, Bergström G, et al. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies. Sci Rep. 2017;7:10425.CrossRef
31.
Zurück zum Zitat Grimby G, Kvist H, Grangård U. Reduction in thigh muscle cross-sectional area and strength in a 4-year follow-up in late polio. Arch Phys Med Rehabil. 1996;77:1044–8.CrossRef Grimby G, Kvist H, Grangård U. Reduction in thigh muscle cross-sectional area and strength in a 4-year follow-up in late polio. Arch Phys Med Rehabil. 1996;77:1044–8.CrossRef
32.
Zurück zum Zitat Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge M-P, Albu J, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (Bethesda, Md : 1985). 2004;97:2333–8. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge M-P, Albu J, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (Bethesda, Md : 1985). 2004;97:2333–8.
33.
Zurück zum Zitat 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.CrossRef 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.CrossRef
Metadaten
Titel
Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment
verfasst von
Robert Hemke
Colleen G. Buckless
Andrew Tsao
Benjamin Wang
Martin Torriani
Publikationsdatum
08.08.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology / Ausgabe 3/2020
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-019-03289-8

Weitere Artikel der Ausgabe 3/2020

Skeletal Radiology 3/2020 Zur Ausgabe

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.