Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medical Imaging
Published in: BMC Medical Imaging 1/2019

Open Access 01-12-2019 | Computed Tomography | Software

CoreSlicer: a web toolkit for analytic morphomics

Authors: Louis Mullie, Jonathan Afilalo

Published in: BMC Medical Imaging | Issue 1/2019

Login to get access

Abstract

Background

Analytic morphomics, or more simply, “morphomics,” refers to the measurement of specific biomarkers of body composition from medical imaging, most commonly computed tomography (CT) images. An emerging body of literature supports the use of morphomic markers measured on single-slice CT images for risk prediction in a range of clinical populations. However, uptake by healthcare providers been limited due to the lack of clinician-friendly software to facilitate measurements. The objectives of this study were to describe the interface and functionality of CoreSlicer- a free and open-source web-based interface aiming to facilitate measurement of analytic morphomics by clinicians - and to validate muscle and fat measurements performed in CoreSlicer against reference software.

Results

Measurements of muscle and fat obtained in CoreSlicer show high agreement with established reference software. CoreSlicer features a full set of DICOM viewing tools and extensible plugin interface to facilitate rapid prototyping and validation of new morphomic markers by researchers. We present published studies illustrating the use of CoreSlicer by clinicians with no prior knowledge of medical image segmentation techniques and no formal training in radiology, where CoreSlicer was successfully used to predict operative risk in three distinct populations of cardiovascular patients.

Conclusions

CoreSlicer enables extraction of morphomic markers from CT images by non-technically skilled clinicians. Measurements were reproducible and accurate in relation to reference software.
Appendix
Available only for authorised users
Literature
1.
Friedman J, Lussiez A, Sullivan J, Wang S, Englesbe M. Implications of sarcopenia in major surgery. Nutr Clin Pract. 2015 Apr;30(2):175–9.PubMedCrossRef
2.
Stidham RW, Waljee AK, Day NM, Bergmans CL, Zahn KM, Higgins PD, et al. Body fat composition assessment using analytic morphomics predicts infectious complications after bowel resection in Crohn's disease. Inflamm Bowel Dis. 2015 Jun;21(6):1306–13.PubMed
3.
Chughtai K, Song Y, Zhang P, Derstine B, Gatza E, Friedman J, et al. Analytic morphomics: a novel CT imaging approach to quantify adipose tissue and muscle composition in allogeneic hematopoietic cell transplantation. Bone Marrow Transplant. 2016 Mar;51(3):446–50.PubMedCrossRef
4.
Singal AG, Zhang P, Waljee AK, Ananthakrishnan L, Parikh ND, Sharma P, et al. Body composition features predict overall survival in patients with hepatocellular carcinoma. Clin Transl Gastroenterol. 2016 May 26;7:e172.PubMedPubMedCentralCrossRef
5.
6.
Smith-Bindman R, Miglioretti DL, Johnson E, Lee C, Feigelson HS, Flynn M, et al. Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996-2010. JAMA. 2012;307(22):2400–9.PubMedCrossRef
7.
8.
9.
Abate N, Burns D, Peshock RM, Garg A, Grundy SM. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res. 1994;35(8):1490–6.PubMed
10.
Martijn van W, Hallvard T. Monitor Changes: Interaction Patterns in User Interfaces: 2000; doi: 10.1.1.36.7484.
11.
Wollny G, Kellman P, Ledesma-Carbayo MJ, Skinner MM, Hublin JJ, Hierl T. MIA - a free and open source software for gray scale medical image analysis. Source Code Biol Med. 2013;8(1):20.PubMedPubMedCentralCrossRef
12.
Gelaude F, Vander Sloten J, Lauwers B. Accuracy assessment of CT-based outer surface femur meshes. Comput Aided Surg. 2008;13(4):188–99.PubMedCrossRef
13.
Heiberg E, Sjögren J, Ugander M, Carlsson M, Engblom H, Arheden H. Design and validation of segment--freely available software for cardiovascular image analysis. BMC Med Imaging. 2010;10:1.PubMedPubMedCentralCrossRef
14.
15.
Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage. 2006;31(3):1116–28.PubMedCrossRef
16.
17.
Susumu M, Dan W, Can C. MRICloud: delivering high-throughput MRI Neuroinformatics as cloud-based software as a service. Computing in Science & Engineering. 2016;18(5):21–35.CrossRef
18.
Sherif T, Rioux P, Rousseau ME, Kassis N, Beck N, Adalat R, et al. CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research. Front Neuroinform. 2014;8:54.PubMedPubMedCentralCrossRef
19.
20.
21.
Sottier D, Petit JM, Guiu S, Hamza S, Benhamiche H, Hillon P, et al. Quantification of the visceral and subcutaneous fat by computed tomography: interobserver correlation of a single slice technique. Diagn Interv Imaging. 2013;94(9):879–84.PubMedCrossRef
22.
Malafarina V, Uriz-Otano F, Iniesta R, Gil-Guerrero L. Effectiveness of nutritional supplementation on muscle mass in treatment of sarcopenia in old age: a systematic review. J Am Med Dir Assoc. 2013;14(1):10–7.PubMedCrossRef
23.
24.
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 (1985). 1998 Jul;85(1):115–22.CrossRef
25.
Zakaria HM, Massie L, Basheer A, Elibe E, Boyce-Fappiano D, Shultz L, et al. Application of Morphometrics as a Predictor for Survival in Patients with Prostate Cancer Metastasis to the Spine. World Neurosurg. 2018.
26.
Zakaria HM, Massie L, Basheer A, Boyce-Fappiano D, Elibe E, Schultz L, et al. Application of morphometrics as a predictor for survival in female patients with breast cancer spinal metastasis: a retrospective cohort study. Spine J. 2018.
27.
Tsutsumi S, Kawahara T, Teranishi JI, Yao M, Uemura H. A low psoas muscle volume predicts longer hospitalization and cancer recurrence in upper urinary tract urothelial carcinoma. Mol Clin Oncol. 2018;8(2):320–2.PubMed
28.
Indrakusuma R, Zijlmans JL, Jalalzadeh H, Planken RN, Balm R, Koelemay MJW. Psoas muscle area as a prognostic factor for survival in patients with an asymptomatic Infrarenal abdominal aortic aneurysm: a retrospective cohort study. Eur J Vasc Endovasc Surg. 2018;55(1):83–91.PubMedCrossRef
29.
Garg L, Agrawal S, Pew T, Hanzel GS, Abbas AE, Gallagher MJ, et al. Psoas muscle area as a predictor of outcomes in Transcatheter aortic valve implantation. Am J Cardiol. 2017;119(3):457–60.PubMedCrossRef
30.
Zuckerman J, Ades M, Mullie L, Trnkus A, Morin JF, Langlois Y, et al. Psoas muscle area and length of stay in older adults undergoing cardiac operations. Ann Thorac Surg. 2017;103(5):1498–504.PubMedCrossRef
31.
Miller BS, Ignatoski KM, Daignault S, Lindland C, Doherty M, Gauger PG, et al. Worsening central sarcopenia and increasing intra-abdominal fat correlate with decreased survival in patients with adrenocortical carcinoma. World J Surg. 2012;36(7):1509–16.PubMedCrossRef
32.
Mamane S, Mullie L, Piazza N, Martucci G, Morais J, Vigano A, et al. Psoas muscle area and all-cause mortality after Transcatheter aortic valve replacement: the Montreal-Munich study. Can J Cardiol. 2016;32(2):177–82.PubMedCrossRef
33.
Peng P, Hyder O, Firoozmand A, Kneuertz P, Schulick RD, Huang D, et al. Impact of sarcopenia on outcomes following resection of pancreatic adenocarcinoma. J Gastrointest Surg. 2012;16(8):1478–86.PubMedPubMedCentralCrossRef
34.
Krell RW, Kaul DR, Martin AR, Englesbe MJ, Sonnenday CJ, Cai S, et al. Association between sarcopenia and the risk of serious infection among adults undergoing liver transplantation. Liver Transpl. 2013;19(12):1396–402.PubMedCrossRef
35.
Smith AB, Deal AM, Yu H, Boyd B, Matthews J, Wallen EM, et al. Sarcopenia as a predictor of complications and survival following radical cystectomy. J Urol. 2014;191(6):1714–20.PubMedCrossRef
36.
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. 2015;22(3):972–9.PubMedCrossRef
37.
Wakabayashi H, Matsushima M, Uwano R, Watanabe N, Oritsu H, Shimizu Y. Skeletal muscle mass is associated with severe dysphagia in cancer patients. J Cachexia Sarcopenia Muscle. 2015;6(4):351–7.PubMedPubMedCentralCrossRef
38.
Drudi LM, Phung K, Ades M, Zuckerman J, Mullie L, Steinmetz OK, et al. Psoas muscle area predicts all-cause mortality after endovascular and open aortic aneurysm repair. Eur J Vasc Endovasc Surg. 2016;52(6):764–9.PubMedCrossRef
39.
Peng PD, van Vledder MG, Tsai S, de Jong MC, Makary M, Ng J, et al. Sarcopenia negatively impacts short-term outcomes in patients undergoing hepatic resection for colorectal liver metastasis. HPB (Oxford). 2011;13(7):439–46.CrossRef
40.
Lee JS, He K, Harbaugh CM, Schaubel DE, Sonnenday CJ, Wang SC, et al. Frailty, core muscle size, and mortality in patients undergoing open abdominal aortic aneurysm repair. J Vasc Surg. 2011;53(4):912–7.PubMedCrossRef
41.
Miller AL, Englesbe MJ, Diehl KM, Chan CL, Cron DC, Derstine BA, et al. Preoperative psoas muscle size predicts postoperative delirium in older adults undergoing surgery: a pilot cohort study. J Am Geriatr Soc. 2017;65(1):e23–4.PubMedCrossRef
42.
Paknikar R, Friedman J, Cron D, Deeb GM, Chetcuti S, Grossman PM, et al. Psoas muscle size as a frailty measure for open and transcatheter aortic valve replacement. J Thorac Cardiovasc Surg. 2016;151(3):745–51.PubMedCrossRef
43.
Sheetz KH, Zhao L, Holcombe SA, Wang SC, Reddy RM, Lin J, et al. Decreased core muscle size is associated with worse patient survival following esophagectomy for cancer. Dis Esophagus. 2013;26(7):716–22.PubMed
44.
Underwood PW, Cron DC, Terjimanian MN, Wang SC, Englesbe MJ, Waits SA. Sarcopenia and failure to rescue following liver transplantation. Clin Transpl. 2015;29(12):1076–80.CrossRef
45.
Caram MV, Bellile EL, Englesbe MJ, Terjimanian M, Wang SC, Griggs JJ, et al. Sarcopenia is associated with autologous transplant-related outcomes in patients with lymphoma. Leuk Lymphoma. 2015;56(10):2855–62.PubMedCrossRef
46.
Sabel MS, Lee J, Cai S, Englesbe MJ, Holcombe S, Wang S. Sarcopenia as a prognostic factor among patients with stage III melanoma. Ann Surg Oncol. 2011;18(13):3579–85.PubMedCrossRef
47.
Hawkins RB, Mehaffey JH, Charles EJ, Kern JA, Lim DS, Teman NR, et al. Psoas Muscle Size Predicts Risk-Adjusted Outcomes After Surgical Aortic Valve Replacement. Ann Thorac Surg. 2018.
48.
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. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:3557–60.
49.
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. 2012;5(1):5–14.PubMedCrossRef
50.
Yamashita M, Kamiya K, Matsunaga A, Kitamura T, Hamazaki N, Matsuzawa R, et al. Prognostic value of psoas muscle area and density in patients who undergo cardiovascular surgery. Can J Cardiol. 2017;33(12):1652–9.PubMedCrossRef
51.
De Amorim BK, Bos SA, Veld J, Lozano-Calderon SA, Torriani M, Bredella MA. Body composition predictors of therapy response in patients with primary extremity soft tissue sarcomas. Acta Radiol. 2018;59(4):478–84.CrossRef
52.
Locke JE, Carr JJ, Nair S, Terry JG, Reed RD, Smith GD, et al. Abdominal lean muscle is associated with lower mortality among kidney waitlist candidates. Clin Transplant. 2017 Mar;31:3.CrossRef
53.
Veld J, Vossen JA, De Amorim BK, Halpern EF, Torriani M, Bredella MA. Adipose tissue and muscle attenuation as novel biomarkers predicting mortality in patients with extremity sarcomas. Eur Radiol. 2016;26(12):4649–55.PubMedCrossRef
54.
Zhang P, Peterson M, Su GL, Wang SC. Visceral adiposity is negatively associated with bone density and muscle attenuation. Am J Clin Nutr. 2015;101(2):337–43.PubMedCrossRef
55.
Torriani M, Hadigan C, Jensen ME, Grinspoon S. Psoas muscle attenuation measurement with computed tomography indicates intramuscular fat accumulation in patients with the HIV-lipodystrophy syndrome. J Appl Physiol (1985). 2003 Sep;95(3):1005–10.CrossRef
56.
DiMartini A, Cruz RJ Jr, Dew MA, Myaskovsky L, Goodpaster B, Fox K, et al. Muscle mass predicts outcomes following liver transplantation. Liver Transpl. 2013;19(11):1172–80.PubMedPubMedCentralCrossRef
57.
Lee CS, Cron DC, Terjimanian MN, Canvasser LD, Mazurek AA, Vonfoerster E, et al. Dorsal muscle group area and surgical outcomes in liver transplantation. Clin Transpl. 2014;28(10):1092–8.CrossRef
58.
Onuma T, Kamishima T, Shimamura T, Kawamura N, Yamashita K, Sutherland K, et al. Longitudinal CT study of sarcopenia due to hepatic failure after living donor liver transplantation. Quant Imaging Med Surg. 2018;8(1):25–31.PubMedPubMedCentralCrossRef
59.
Canvasser LD, Mazurek AA, Cron DC, Terjimanian MN, Chang ET, Lee CS, et al. Paraspinous muscle as a predictor of surgical outcome. J Surg Res. 2014;192(1):76–81.PubMedCrossRef
60.
61.
Fortin M, Omidyeganeh M, Battié MC, Ahmad O, Rivaz H. Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images. Biomed Eng Online. 2017;16(1):61.PubMedPubMedCentralCrossRef
62.
63.
Battaglia N V, Mahfouz M R, Johnson M. Semi-Automatic segmentation of the lumbar muscles and gender specific cross-sectional areas (Abstract). 58th Annual Meeting of the Orthopaedic. Research Society, San Francisco, CA, 2012.
64.
Otemuyiwa B, Derstine BA, Zhang P, Wong SL, Sabel MS, Redman BG, et al. Dorsal muscle attenuation may predict failure to respond to Interleukin-2 therapy in metastatic renal cell carcinoma. Acad Radiol. 2017 Sep;24(9):1094–100.PubMedCrossRef
65.
Weijs PJ, Looijaard WG, 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. 2014;18(2):R12.PubMedPubMedCentralCrossRef
66.
Englesbe MJ, Lee JS, He K, Fan L, Schaubel DE, Sheetz KH, et al. Analytic morphomics, core muscle size, and surgical outcomes. Ann Surg. 2012 Aug;256(2):255–61.PubMedCrossRef
67.
68.
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(2):512–20.PubMedCrossRef
69.
Silva de Paula N, de Aguiar BK, Azevedo Aredes M, Villaça Chaves G. Sarcopenia and skeletal muscle quality as predictors of postoperative complication and early mortality in gynecologic Cancer. Int J Gynecol Cancer. 2018;28(2):412–20.PubMedCrossRef
70.
Lee S, Paik HC, Haam SJ, Lee CY, Nam KS, Jung HS, et al. Sarcopenia of thoracic muscle mass is not a risk factor for survival in lung transplant recipients. J Thorac Dis. 2016 Aug;8(8):2011–7.PubMedPubMedCentralCrossRef
71.
Derstine BA, Holcombe SA, Goulson RL, Ross BE, Wang NC, Sullivan JA, et al. Quantifying sarcopenia reference values using lumbar and thoracic muscle areas in a healthy population. J Nutr Health Aging. 2017;21(10):180–5.PubMed
72.
Miller JA, Harris K, Roche C, Dhillon S, Battoo A, Demmy T, et al. Sarcopenia is a predictor of outcomes after lobectomy. J Thorac Dis. 2018 Jan;10(1):432–40.PubMedPubMedCentralCrossRef
73.
Fintelmann FJ, Troschel FM, Mario J, Chretien YR, Knoll SJ, Muniappan A, et al. Thoracic Skeletal Muscle Is Associated With Adverse Outcomes After Lobectomy for Lung Cancer. Ann Thorac Surg. 2018.
74.
Karteek P, Dana C, Martin J, Nina E, Vickie B. Fem-based automatic segmentation of muscle and fat tissues from thoracic CT images. IEEE Trans Med Imaging. 2016;35(2):512–20.CrossRef
75.
Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000;21(6):697–738.PubMedCrossRef
76.
Guiu B, Petit JM, Bonnetain F, Ladoire S, Guiu S, Cercueil JP, et al. Visceral fat area is an independent predictive biomarker of outcome after first-line bevacizumab-based treatment in metastatic colorectal cancer. Gut. 2010;59(3):341–7.PubMedCrossRef
77.
Ladoire S, Bonnetain F, Gauthier M, Zanetta S, Petit JM, Guiu S, et al. Visceral fat area as a new independent predictive factor of survival in patients with metastatic renal cell carcinoma treated with antiangiogenic agents. Oncologist. 2011;16(1):71–81.PubMedPubMedCentralCrossRef
78.
Ryo M, Kishida K, Nakamura T, Yoshizumi T, Funahashi T, Shimomura I. Clinical significance of visceral adiposity assessed by computed tomography: a Japanese perspective. World J Radiol. 2014;6(7):409–16.PubMedPubMedCentralCrossRef
79.
Cakir H, Heus C, van der Ploeg TJ, Houdijk AP. Visceral obesity determined by CT scan and outcomes after colorectal surgery; a systematic review and meta-analysis. Int J Color Dis. 2015;30(7):875–82.CrossRef
80.
Demerath EW, Ritter KJ, Couch WA, Rogers NL, Moreno GM, Choh A, et al. Validity of a new automated software program for visceral adipose tissue estimation. Int J Obes. 2007 Feb;31(2):285–91.CrossRef
81.
82.
Nemoto M, Yeernuer T, Masutani Y, Nomura Y, Hanaoka S, Miki S, et al. Development of automatic visceral fat volume calculation software for CT volume data. J Obes. 2014;2014:495084.PubMedPubMedCentralCrossRef
83.
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(2):155–62.PubMedCrossRef
84.
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. 2006;30(5):777–83.PubMedCrossRef
85.
Pan-Fu K, Kuo Y-L, Po-Tsun L, Wei-Chen C, Ya-Ling H, Chiun-li C. Fully automatic abdominal fat segmentation system from a low resolution CT image. J Comput. 2015;36(2):65–77.
86.
Lee SJ, Liu J, Yao J, Kanarek A, Summers RM, Pickhardt PJ. Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort. Br J Radiol. 2018;28:20170968.CrossRef
87.
Rosenquist KJ, Pedley A, Massaro JM, Therkelsen KE, Murabito JM, Hoffmann U, et al. Visceral and subcutaneous fat quality and cardiometabolic risk. JACC Cardiovasc Imaging. 2013;6(7):762–71.PubMedPubMedCentralCrossRef
88.
Shah RV, Allison MA, Lima JA, Abbasi SA, Eisman A, Lai C, et al. Abdominal fat radiodensity, quantity and cardiometabolic risk: the multi-ethnic study of atherosclerosis. Nutr Metab Cardiovasc Dis. 2016;26(2):114–22.PubMedCrossRef
89.
Therkelsen KE, Pedley A, Rosenquist KJ, Hoffmann U, Massaro JM, Murabito JM, et al. Adipose tissue attenuation as a marker of adipose tissue quality: associations with six-year changes in body weight. Obesity (Silver Spring). 2016;24(2):499–505.CrossRef
90.
Yeoh AJ, Pedley A, Rosenquist KJ, Hoffmann U, Fox CS. The association between subcutaneous fat density and the propensity to store fat viscerally. J Clin Endocrinol Metab. 2015;100(8):E1056–64.PubMedPubMedCentralCrossRef
91.
Murabito JM, Pedley A, Massaro JM, Vasan RS, Esliger D, Blease SJ, et al. Moderate-to-vigorous physical activity with accelerometry is associated with visceral adipose tissue in adults. J Am Heart Assoc. 2015;4(3):e001379.PubMedPubMedCentralCrossRef
92.
Rosenquist KJ, Massaro JM, Pedley A, Long MT, Kreger BE, Vasan RS, et al. Fat quality and incident cardiovascular disease, all-cause mortality, and cancer mortality. J Clin Endocrinol Metab. 2015;100(1):227–34.PubMedCrossRef
93.
Alvey NJ, Pedley A, Rosenquist KJ, Massaro JM, O'Donnell CJ, Hoffmann U, et al. Association of fat density with subclinical atherosclerosis. J Am Heart Assoc. 2014;3:4.CrossRef
94.
Yong HS, Kim EJ, Seo HS, Kang EY, Kim YK, Woo OH, et al. Pericardial fat is more abundant in patients with coronary atherosclerosis and even in the non-obese patients: evaluation with cardiac CT angiography. Int J Cardiovasc Imaging. 2010;26(Suppl 1):53–62.PubMedCrossRef
95.
Jang HC, Lee HK, Lee H, Cha JG, Kim YS, Cho JH. Analyzing correlation between epicardial fat area and metabolic syndrome risk factor by using low-dose lung CT. Pak J Med Sci. 2015;31(5):1207–12.PubMedPubMedCentralCrossRef
96.
Song DK, Hong YS, Lee H, Oh JY, Sung YA, Kim Y. Increased Epicardial adipose tissue thickness in type 2 diabetes mellitus and obesity. Diabetes Metab J. 2015;39(5):405–13.PubMedPubMedCentralCrossRef
97.
Balci A, Celik M, Balci DD, Karazincir S, Yonden Z, Korkmaz I, et al. Patients with psoriasis have an increased amount of epicardial fat tissue. Clin Exp Dermatol. 2014;39(2):123–8.PubMedCrossRef
98.
Dagvasumberel M, Shimabukuro M, Nishiuchi T, Ueno J, Takao S, Fukuda D, et al. Gender disparities in the association between epicardial adipose tissue volume and coronary atherosclerosis: a 3-dimensional cardiac computed tomography imaging study in Japanese subjects. Cardiovasc Diabetol. 2012;11:106.PubMedPubMedCentralCrossRef
99.
Oyama N, Goto D, Ito YM, Ishimori N, Mimura R, Furumoto T, et al. Single-slice epicardial fat area measurement: do we need to measure the total epicardial fat volume? Jpn J Radiol. 2011;29(2):104–9.PubMedCrossRef
100.
Maimaituxun G, Shimabukuro M, Fukuda D, Yagi S, Hirata Y, Iwase T, et al. Local thickness of Epicardial adipose tissue surrounding the left anterior descending artery is a simple predictor of coronary artery disease - new prediction model in combination with Framingham risk score. Circ J. 2018;21.
101.
Norlén A, Alvén J, Molnar D, Enqvist O, Norrlund RR, Brandberg J, et al. Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography, 034003. J Med Imaging (Bellingham). 2016;3(3).
102.
Rodrigues EO, Morais FFC, Morais NA, Coni LS, Neto LV, Conci A. A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography. Comput Methods Prog Biomed. 2016;123:109–28.CrossRef
103.
Coppini G, Favilla R, Marraccini P, Moroni D, Pieri G. Quantification of Epicardial fat by cardiac CT imaging. Open Med Inform J. 2010;4:126–35.PubMedPubMedCentral
104.
105.
Bandekar AN, Naghavi M, Kakadiaris IA. Automated pericardial fat quantification in CT data. Conf Proc IEEE Eng Med Biol Soc. 2006;1:932–5.CrossRef
106.
Abazida O. H Saqqaab. Epicardial fat quality effect on subclinical atherosclerosis. J Saudi Heart Assoc. 2016;28(3):129–220.CrossRef
Metadata
Title
CoreSlicer: a web toolkit for analytic morphomics
Authors
Louis Mullie
Jonathan Afilalo
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2019
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-019-0316-6

Other articles of this Issue 1/2019

BMC Medical Imaging 1/2019 Go to the issue

Study protocol

Localising occult prostate cancer metastasis with advanced imaging techniques (LOCATE trial): a prospective cohort, observational diagnostic accuracy trial investigating whole–body magnetic resonance imaging in radio-recurrent prostate cancer

Research article

Repeatability and reproducibility of cerebral 23Na imaging in healthy subjects

Research article

Reduced scan range abdominopelvic CT in patients with suspected acute appendicitis - impact on diagnostic accuracy and effective radiation dose

Research article

Value of virtual monochromatic spectral image of dual-layer spectral detector CT with noise reduction algorithm for image quality improvement in obese simulated body phantom

Research article

Low-grade Myofibroblastic sarcoma: clinical and imaging findings

Research article

Feasibility evaluation of micro-optical coherence tomography (μOCT) for rapid brain tumor type and grade discriminations: μOCT images versus pathology