{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T16:39:06Z","timestamp":1768408746961,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,6,26]],"date-time":"2017-06-26T00:00:00Z","timestamp":1498435200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2017,8]]},"DOI":"10.1007\/s10278-017-9988-z","type":"journal-article","created":{"date-parts":[[2017,6,26]],"date-time":"2017-06-26T15:38:00Z","timestamp":1498491480000},"page":"487-498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis"],"prefix":"10.1007","volume":"30","author":[{"given":"Hyunkwang","family":"Lee","sequence":"first","affiliation":[]},{"given":"Fabian M.","family":"Troschel","sequence":"additional","affiliation":[]},{"given":"Shahein","family":"Tajmir","sequence":"additional","affiliation":[]},{"given":"Georg","family":"Fuchs","sequence":"additional","affiliation":[]},{"given":"Julia","family":"Mario","sequence":"additional","affiliation":[]},{"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[]},{"given":"Synho","family":"Do","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,6,26]]},"reference":[{"key":"9988_CR1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S1470-2045(08)70153-0","volume":"9","author":"CMM Prado","year":"2008","unstructured":"Prado CMM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L et al.: Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9:629\u2013635, 2008","journal-title":"Lancet Oncol"},{"key":"9988_CR2","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1200\/JCO.2012.45.2722","volume":"31","author":"L Martin","year":"2013","unstructured":"Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin MT, McCargar LJ et al.: Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 31:1539\u20131547, 2013","journal-title":"J Clin Oncol"},{"key":"9988_CR3","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1200\/JCO.2015.63.6043","volume":"34","author":"S Blauwhoff-Buskermolen","year":"2016","unstructured":"Blauwhoff-Buskermolen S, Versteeg KS, de van der Schueren MAE, den Braver NR, Berkhof J, Langius JAE et al.: Loss of muscle mass during chemotherapy is predictive for poor survival of patients with metastatic colorectal cancer. J Clin Oncol 34:1339\u20131344, 2016","journal-title":"J Clin Oncol"},{"key":"9988_CR4","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1148\/radiol.2016160626","volume":"282","author":"AM McDonald","year":"2017","unstructured":"McDonald AM, Swain TA, Mayhew DL, Cardan RA, Baker CB, Harris DM et al.: CT measures of bone mineral density and muscle mass can be used to predict noncancer death in men with prostate cancer. Radiology 282:475\u2013483, 2017","journal-title":"Radiology"},{"key":"9988_CR5","doi-asserted-by":"crossref","first-page":"R206","DOI":"10.1186\/cc12901","volume":"17","author":"LL Moisey","year":"2013","unstructured":"Moisey LL, Mourtzakis M, Cotton BA, Premji T, Heyland DK, Wade CE et al.: Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients. Crit Care 17:R206, 2013","journal-title":"Crit Care"},{"key":"9988_CR6","doi-asserted-by":"crossref","first-page":"R12","DOI":"10.1186\/cc13189","volume":"18","author":"PJM Weijs","year":"2014","unstructured":"Weijs PJM, Looijaard WGPM, Dekker IM, Stapel SN, Girbes AR, Oudemans-van Straaten HM et al.: Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Crit Care 18:R12, 2014","journal-title":"Crit Care"},{"key":"9988_CR7","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jamcollsurg.2010.03.039","volume":"211","author":"MJ Englesbe","year":"2010","unstructured":"Englesbe MJ, Patel SP, He K, Lynch RJ, Schaubel DE, Harbaugh C et al.: Sarcopenia and mortality after liver transplantation. J Am Coll Surg 211:271\u2013278, 2010","journal-title":"J Am Coll Surg"},{"key":"9988_CR8","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1097\/SLA.0000000000000628","volume":"261","author":"KW Reisinger","year":"2015","unstructured":"Reisinger KW, van Vugt JLA, Tegels JJW, Snijders C, Hulsew\u00e9 KWE, Hoofwijk AGM et al.: Functional compromise reflected by sarcopenia, frailty, and nutritional depletion predicts adverse postoperative outcome after colorectal cancer surgery. Ann Surg 261:345\u2013352, 2015","journal-title":"Ann Surg"},{"key":"9988_CR9","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1245\/s10434-014-4040-8","volume":"22","author":"LM Kuroki","year":"2015","unstructured":"Kuroki LM, Mangano M, Allsworth JE, Menias CO, Massad LS, Powell MA et al.: Pre-operative assessment of muscle mass to predict surgical complications and prognosis in patients with endometrial cancer. Ann Surg Oncol 22:972\u2013979, 2015","journal-title":"Ann Surg Oncol"},{"key":"9988_CR10","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1002\/bjs.10063","volume":"103","author":"N Pecorelli","year":"2016","unstructured":"Pecorelli N, Carrara G, De Cobelli F, Cristel G, Damascelli A, Balzano G et al.: Effect of sarcopenia and visceral obesity on mortality and pancreatic fistula following pancreatic cancer surgery. Br J Surg 103:434\u2013442, 2016","journal-title":"Br J Surg"},{"key":"9988_CR11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1152\/jappl.1998.85.1.115","volume":"85","author":"N Mitsiopoulos","year":"1998","unstructured":"Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R: Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 85:115\u2013122, 1998","journal-title":"J Appl Physiol"},{"key":"9988_CR12","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1152\/japplphysiol.00744.2004","volume":"97","author":"W Shen","year":"2004","unstructured":"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 97:2333\u20132338, 2004","journal-title":"J Appl Physiol"},{"key":"9988_CR13","doi-asserted-by":"crossref","first-page":"W255","DOI":"10.2214\/AJR.15.14635","volume":"205","author":"RD Boutin","year":"2015","unstructured":"Boutin RD, Yao L, Canter RJ, Lenchik L: Sarcopenia: current concepts and imaging implications. AJR Am J Roentgenol 205:W255\u2013W266, 2015","journal-title":"AJR Am J Roentgenol"},{"key":"9988_CR14","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1097\/01.rct.0000228164.08968.e8","volume":"30","author":"B Zhao","year":"2006","unstructured":"Zhao B, Colville J, Kalaigian J, Curran S, Jiang L, Kijewski P et al.: Automated quantification of body fat distribution on volumetric computed tomography. J Comput Assist Tomogr 30:777\u2013783, 2006","journal-title":"J Comput Assist Tomogr"},{"key":"9988_CR15","doi-asserted-by":"crossref","unstructured":"Kamiya N, Zhou X, Chen H, Hara T, Hoshi H, Yokoyama R et al.: Automated recognition of the psoas major muscles on X-ray CT images.In Engineering in Medicine and Biology Society, EMBC. 2009, pp. 3557\u20133560","DOI":"10.1109\/IEMBS.2009.5332597"},{"key":"9988_CR16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s12194-011-0127-0","volume":"5","author":"N Kamiya","year":"2012","unstructured":"Kamiya N, Zhou X, Chen H, Muramatsu C, Hara T, Yokoyama R et al.: Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study. Radiol Phys Technol 5:5\u201314, 2012","journal-title":"Radiol Phys Technol"},{"key":"9988_CR17","doi-asserted-by":"crossref","unstructured":"Kamiya N, Zhou X, Chen H, Muramatsu C, Hara T, Yokoyama R et al.: Automated segmentation of recuts abdominis muscle using shape model in X-ray CT images. In Engineering in Medicine and Biology Society, EMBC. 2011, pp. 7993\u20137996","DOI":"10.1109\/IEMBS.2011.6091971"},{"key":"9988_CR18","doi-asserted-by":"publisher","unstructured":"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. SPIE Medical Imaging. International Society for Optics and Photonics 72610K\u201372610K\u20138, 2009. doi: 10.1117\/12.812412","DOI":"10.1117\/12.812412"},{"key":"9988_CR19","doi-asserted-by":"crossref","unstructured":"Popuri K, Cobzas D, J\u00e4gersand M, Esfandiari N, Baracos V: FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images. 2013 I.E. 10th International Symposium on Biomedical Imaging. 2013, pp 149\u2013152","DOI":"10.1109\/ISBI.2013.6556434"},{"key":"9988_CR20","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TMI.2015.2479252","volume":"35","author":"K Popuri","year":"2016","unstructured":"Popuri K, Cobzas D, Esfandiari N, Baracos V, J\u00e4gersand M: Body composition assessment in axial CT images using FEM-based automatic segmentation of skeletal muscle. IEEE Trans Med Imaging 35:512\u2013520, 2016","journal-title":"IEEE Trans Med Imaging"},{"key":"9988_CR21","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1088\/0031-9155\/61\/17\/6553","volume":"61","author":"DF Polan","year":"2016","unstructured":"Polan DF, Brady SL, Kaufman RA: Tissue segmentation of computed tomography images using a random Forest algorithm: a feasibility study. Phys Med Biol 61:6553\u20136569, 2016","journal-title":"Phys Med Biol"},{"key":"9988_CR22","doi-asserted-by":"publisher","unstructured":"Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207\u20131216, 2016. doi: 10.1109\/TMI.2016.2535865","DOI":"10.1109\/TMI.2016.2535865"},{"key":"9988_CR23","doi-asserted-by":"publisher","unstructured":"Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK et al.: Fully automated deep learning system for bone age assessment. J Digit Imaging 8:1\u20135, 2017. doi: 10.1007\/s10278-017-9955-8","DOI":"10.1007\/s10278-017-9955-8"},{"key":"9988_CR24","doi-asserted-by":"publisher","unstructured":"Pereira S, Pinto A, Alves V, Silva CA: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240\u20131251, 2016. doi: 10.1109\/TMI.2016.2538465","DOI":"10.1109\/TMI.2016.2538465"},{"key":"9988_CR25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y et al.: Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331, 2017","journal-title":"Med Image Anal"},{"key":"9988_CR26","doi-asserted-by":"publisher","unstructured":"Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Isgum I: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252\u201361, 2016. doi: 10.1109\/TMI.2016.2548501","DOI":"10.1109\/TMI.2016.2548501"},{"key":"9988_CR27","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TMI.2016.2519264","volume":"35","author":"Y Gao","year":"2016","unstructured":"Gao Y, Shao Y, Lian J, Wang AZ, Chen RC, Shen D: Accurate segmentation of CT male pelvic organs via regression-based deformable models and multi-task random forests. IEEE Trans Med Imaging 35:1532\u20131543, 2016","journal-title":"IEEE Trans Med Imaging"},{"key":"9988_CR28","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB et al.: Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2015 Oct 5, Springer International Publishing, 2015, pp. 556\u2013564","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"9988_CR29","doi-asserted-by":"publisher","unstructured":"Liskowski P, Pawel L, Krzysztof K: Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369-2380, 2016. doi: 10.1109\/TMI.2016.2546227","DOI":"10.1109\/TMI.2016.2546227"},{"key":"9988_CR30","doi-asserted-by":"crossref","unstructured":"Greenspan H, van Ginneken B, Summers RM: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35:1153\u20131159","DOI":"10.1109\/TMI.2016.2553401"},{"key":"9988_CR31","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T: Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3431\u20133440, 2015","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9988_CR32","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s10554-011-9848-8","volume":"28","author":"OL Segev","year":"2012","unstructured":"Segev OL, Gaspar T, Halon DA, Peled N, Domachevsky L, Lewis BS et al.: Image quality in obese patients undergoing 256-row computed tomography coronary angiography. Int J Card Imaging 28:633\u2013639, 2012","journal-title":"Int J Card Imaging"},{"key":"9988_CR33","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1007\/s10278-006-1052-3","volume":"20","author":"T Kimpe","year":"2007","unstructured":"Kimpe T, Tuytschaever T. Increasing the number of gray shades in medical display systems\u2014how much is enough? J Digit Imaging 20:422\u2013432, 2007","journal-title":"J Digit Imaging"},{"key":"9988_CR34","doi-asserted-by":"publisher","unstructured":"Dodge S, Karam L: Understanding how image quality affects deep neural networks. Quality of Multimedia Experience (QoMEX), 2016 Eighth International Conference on. IEEE 1\u20136. doi: 10.1109\/QoMEX.2016.7498955","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"9988_CR35","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T: Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640\u2013651, 2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9988_CR36","unstructured":"NVIDIA\u00ae DIGITS\u2122 DevBox. In: NVIDIA Developer [Internet]. Available: https:\/\/developer.nvidia.com\/devbox , 16 Mar 2015 [cited 23 Aug 2016]"},{"key":"9988_CR37","doi-asserted-by":"crossref","unstructured":"Cimpoi M, Maji S, Vedaldi A: Deep filter banks for texture recognition and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015, pp 3828\u20133836","DOI":"10.1109\/CVPR.2015.7299007"},{"key":"9988_CR38","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.patrec.2016.08.016","volume":"84","author":"V Andrearczyk","year":"2016","unstructured":"Andrearczyk V, Whelan PF: Using filter banks in convolutional neural networks for texture classification. Pattern Recogn Lett 84:63\u201369, 2016","journal-title":"Pattern Recogn Lett"},{"key":"9988_CR39","unstructured":"2016 CT Market Outlook Report. In: IMVInfo.com [Internet]. Available: http:\/\/www.imvinfo.com\/index.aspx?sec=ct&sub=dis&itemid=200081 , [cited 14 Mar 2017]"},{"key":"9988_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1259\/bjr\/38447238","volume":"85","author":"A Shuster","year":"2012","unstructured":"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 85:1\u201310, 2012","journal-title":"Br J Radiol"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-017-9988-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-017-9988-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-017-9988-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:17:47Z","timestamp":1569554267000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-017-9988-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,26]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["9988"],"URL":"https:\/\/doi.org\/10.1007\/s10278-017-9988-z","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,26]]}}}