{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T23:51:29Z","timestamp":1788393089144,"version":"build-2803163510"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the research fund of Chungnam National University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"Abstract<\/jats:title>\n \n In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle\/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420\u20130.875), and 13.2% were correlated with the SCDR (0.406\u20130.460). The mean subindex of Index 2\n \n \n $$\\left( {\\frac{{P_{Fibrosis\\, in\\, Affected} - P_{Fibrosis\\, in\\, Unaffected} }}{{P_{Limb\\, in\\, Unaffected} }}} \\right)$$<\/jats:tex-math>\n \n \n \n \n \n P<\/mml:mi>\n \n F<\/mml:mi>\n i<\/mml:mi>\n b<\/mml:mi>\n r<\/mml:mi>\n o<\/mml:mi>\n s<\/mml:mi>\n i<\/mml:mi>\n s<\/mml:mi>\n \n i<\/mml:mi>\n n<\/mml:mi>\n \n A<\/mml:mi>\n f<\/mml:mi>\n f<\/mml:mi>\n e<\/mml:mi>\n c<\/mml:mi>\n t<\/mml:mi>\n e<\/mml:mi>\n d<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n -<\/mml:mo>\n \n P<\/mml:mi>\n \n F<\/mml:mi>\n i<\/mml:mi>\n b<\/mml:mi>\n r<\/mml:mi>\n o<\/mml:mi>\n s<\/mml:mi>\n i<\/mml:mi>\n s<\/mml:mi>\n \n i<\/mml:mi>\n n<\/mml:mi>\n \n U<\/mml:mi>\n n<\/mml:mi>\n a<\/mml:mi>\n f<\/mml:mi>\n f<\/mml:mi>\n e<\/mml:mi>\n c<\/mml:mi>\n t<\/mml:mi>\n e<\/mml:mi>\n d<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n \n P<\/mml:mi>\n \n L<\/mml:mi>\n i<\/mml:mi>\n m<\/mml:mi>\n b<\/mml:mi>\n \n i<\/mml:mi>\n n<\/mml:mi>\n \n U<\/mml:mi>\n n<\/mml:mi>\n a<\/mml:mi>\n f<\/mml:mi>\n f<\/mml:mi>\n e<\/mml:mi>\n c<\/mml:mi>\n t<\/mml:mi>\n e<\/mml:mi>\n d<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mfrac>\n <\/mml:mfenced>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.\n <\/jats:p>","DOI":"10.1038\/s41598-022-19204-6","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T06:23:57Z","timestamp":1663050237000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images"],"prefix":"10.1038","volume":"12","author":[{"given":"Hyewon","family":"Son","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suwon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwangsoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-9218","authenticated-orcid":false,"given":"Kyo-in","family":"Koo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-3602","authenticated-orcid":false,"given":"Chang Ho","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"19204_CR1","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1089\/lrb.2019.0014","volume":"17","author":"DH Keast","year":"2019","unstructured":"Keast, D. H., Moffatt, C. & Janmohammad, A. Lymphedema impact and prevalence international study: The Canadian data. Lymphat. Res. Biol. 17, 178\u2013186. https:\/\/doi.org\/10.1089\/lrb.2019.0014 (2019).","journal-title":"Lymphat. Res. Biol."},{"key":"19204_CR2","doi-asserted-by":"publisher","first-page":"462","DOI":"10.3174\/ajnr.A2822","volume":"33","author":"ME Spector","year":"2012","unstructured":"Spector, M. E., Gallagher, K. K., McHugh, J. B. & Mukherji, S. K. Correlation of radiographic and pathologic findings of dermal lymphatic invasion in head and neck squamous cell carcinoma. AJNR Am. J. Neuroradiol. 33, 462\u2013464. https:\/\/doi.org\/10.3174\/ajnr.A2822 (2012).","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"19204_CR3","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1097\/rlu.0000000000001525","volume":"42","author":"J Wang","year":"2017","unstructured":"Wang, J., Iranmanesh, A. M. & Oates, M. E. Skeletal scintigraphy in radiation-induced fibrosis with lymphedema. Clin. Nucl. Med. 42, 231\u2013234. https:\/\/doi.org\/10.1097\/rlu.0000000000001525 (2017).","journal-title":"Clin. Nucl. Med."},{"key":"19204_CR4","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/s10147-014-0724-0","volume":"20","author":"I Deura","year":"2015","unstructured":"Deura, I. et al. Incidence and risk factors for lower limb lymphedema after gynecologic cancer surgery with initiation of periodic complex decongestive physiotherapy. Int. J. Clin. Oncol. 20, 556\u2013560. https:\/\/doi.org\/10.1007\/s10147-014-0724-0 (2015).","journal-title":"Int. J. Clin. Oncol."},{"key":"19204_CR5","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1097\/aog.0000000000000372","volume":"124","author":"KJ Yost","year":"2014","unstructured":"Yost, K. J. et al. Lymphedema after surgery for endometrial cancer: Prevalence, risk factors, and quality of life. Obstet. Gynecol. 124, 307\u2013315. https:\/\/doi.org\/10.1097\/aog.0000000000000372 (2014).","journal-title":"Obstet. Gynecol."},{"key":"19204_CR6","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1634\/theoncologist.2012-0387","volume":"18","author":"J O'Toole","year":"2013","unstructured":"O\u2019Toole, J. et al. Screening for breast cancer-related lymphedema: The need for standardization. The Oncologist 18, 350\u2013352. https:\/\/doi.org\/10.1634\/theoncologist.2012-0387 (2013).","journal-title":"The Oncologist"},{"key":"19204_CR7","first-page":"565","volume":"17","author":"H Sisman","year":"2012","unstructured":"Sisman, H., Sahin, B., Duman, B. B. & Tanriverdi, G. Nurse-assisted education and exercise decrease the prevalence and morbidity of lymphedema following breast cancer surgery. J. B.U.ON. Off. J. Balk. Union Oncol. 17, 565\u2013569 (2012).","journal-title":"J. B.U.ON. Off. J. Balk. Union Oncol."},{"key":"19204_CR8","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1186\/1748-717x-8-271","volume":"8","author":"E Rasmusson","year":"2013","unstructured":"Rasmusson, E. et al. Low rate of lymphedema after extended pelvic lymphadenectomy followed by pelvic irradiation of node-positive prostate cancer. Radiat. Oncol. (London, England) 8, 271. https:\/\/doi.org\/10.1186\/1748-717x-8-271 (2013).","journal-title":"Radiat. Oncol. (London, England)"},{"key":"19204_CR9","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1097\/prs.0000000000003024","volume":"139","author":"MC van Zanten","year":"2017","unstructured":"van Zanten, M. C. et al. The lymphatic response to injury with soft-tissue reconstruction in high-energy open tibial fractures of the lower extremity. Plast. Reconstr. Surg. 139, 483\u2013491. https:\/\/doi.org\/10.1097\/prs.0000000000003024 (2017).","journal-title":"Plast. Reconstr. Surg."},{"key":"19204_CR10","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1089\/lrb.2013.1132","volume":"11","author":"SG Rockson","year":"2013","unstructured":"Rockson, S. G. The lymphatics and the inflammatory response: Lessons learned from human lymphedema. Lymphat. Res. Biol. 11, 117\u2013120. https:\/\/doi.org\/10.1089\/lrb.2013.1132 (2013).","journal-title":"Lymphat. Res. Biol."},{"key":"19204_CR11","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1038\/nature04480","volume":"438","author":"K Alitalo","year":"2005","unstructured":"Alitalo, K., Tammela, T. & Petrova, T. V. Lymphangiogenesis in development and human disease. Nature 438, 946\u2013953. https:\/\/doi.org\/10.1038\/nature04480 (2005).","journal-title":"Nature"},{"key":"19204_CR12","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1097\/00129334-200404000-00014","volume":"17","author":"MJ Geyer","year":"2004","unstructured":"Geyer, M. J., Brienza, D. M., Chib, V. & Wang, J. Quantifying fibrosis in venous disease: Mechanical properties of lipodermatosclerotic and healthy tissue. Adv. Skin Wound Care 17, 131\u2013142 (2004).","journal-title":"Adv. Skin Wound Care"},{"key":"19204_CR13","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3400\/avd.oa.12.00102","volume":"6","author":"K Suehiro","year":"2013","unstructured":"Suehiro, K. et al. Significance of ultrasound examination of skin and subcutaneous tissue in secondary lower extremity lymphedema. Ann. Vasc. Dis. 6, 180\u2013188. https:\/\/doi.org\/10.3400\/avd.oa.12.00102 (2013).","journal-title":"Ann. Vasc. Dis."},{"key":"19204_CR14","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1148\/radiology.149.1.6611927","volume":"149","author":"JL Gamba","year":"1983","unstructured":"Gamba, J. L., Silverman, P. M., Ling, D., Dunnick, N. R. & Korobkin, M. Primary lower extremity lymphedema: CT diagnosis. Radiology 149, 218. https:\/\/doi.org\/10.1148\/radiology.149.1.6611927 (1983).","journal-title":"Radiology"},{"key":"19204_CR15","first-page":"2220","volume":"102","author":"A Tenenbaum","year":"2005","unstructured":"Tenenbaum, A., Brorson, H., Johansson, E., Perbeck, L. & Steen-Zupanc, U. Lower risk of fat formation and fibrosis if lymphedema is treated in time. Lakartidningen 102, 2220\u20132225 (2005).","journal-title":"Lakartidningen"},{"key":"19204_CR16","first-page":"121","volume":"35","author":"ED Monnin-Delhom","year":"2002","unstructured":"Monnin-Delhom, E. D., Gallix, B. P., Achard, C., Bruel, J. M. & Janbon, C. High resolution unenhanced computed tomography in patients with swollen legs. Lymphology 35, 121\u2013128 (2002).","journal-title":"Lymphology"},{"key":"19204_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/S0009-9260(05)80927-4","volume":"41","author":"BF Vaughan","year":"1990","unstructured":"Vaughan, B. F. CT of swollen legs. Clin. Radiol. 41, 24\u201330 (1990).","journal-title":"Clin. Radiol."},{"key":"19204_CR18","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1111\/bjd.15238","volume":"177","author":"K Tashiro","year":"2017","unstructured":"Tashiro, K. et al. Pathological changes of adipose tissue in secondary lymphoedema. Br. J. Dermatol. 177, 158\u2013167. https:\/\/doi.org\/10.1111\/bjd.15238 (2017).","journal-title":"Br. J. Dermatol."},{"key":"19204_CR19","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1126\/science.6997993","volume":"210","author":"GN Hounsfield","year":"1980","unstructured":"Hounsfield, G. N. Computed medical imaging. Science (New York, N.Y.) 210, 22\u201328 (1980).","journal-title":"Science (New York, N.Y.)"},{"key":"19204_CR20","doi-asserted-by":"publisher","first-page":"488","DOI":"10.5535\/arm.2015.39.3.488","volume":"39","author":"SY Kim","year":"2015","unstructured":"Kim, S. Y., Bae, H. & Ji, H. M. Computed tomography as an objective measurement tool for secondary lymphedema treated with extracorporeal shock wave therapy. Ann. Rehabil. Med. 39, 488\u2013493. https:\/\/doi.org\/10.5535\/arm.2015.39.3.488 (2015).","journal-title":"Ann. Rehabil. Med."},{"key":"19204_CR21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224457","volume":"14","author":"KI Koo","year":"2019","unstructured":"Koo, K. I. et al. Comparison of a novel algorithm quantitatively estimating epifascial fibrosis in three-dimensional computed tomography images to other clinical lymphedema grading methods. PLoS ONE 14, e0224457. https:\/\/doi.org\/10.1371\/journal.pone.0224457 (2019).","journal-title":"PLoS ONE"},{"key":"19204_CR22","doi-asserted-by":"publisher","DOI":"10.3802\/jgo.2017.28.e18","volume":"28","author":"JS Yoo","year":"2017","unstructured":"Yoo, J. S., Chung, S. H. & Lim, M. C. Computed tomography-based quantitative assessment of lower extremity lymphedema following treatment for gynecologic cancer. J. Gynecol. Oncol. 28, e18. https:\/\/doi.org\/10.3802\/jgo.2017.28.e18 (2017).","journal-title":"J. Gynecol. Oncol."},{"key":"19204_CR23","first-page":"305","volume":"4","author":"KH Fletcher","year":"1951","unstructured":"Fletcher, K. H. Matter with a mind; A neurological research robot. Res. J. Sci. Appl. 4, 305\u2013307 (1951).","journal-title":"Res. J. Sci. Appl."},{"key":"19204_CR24","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1118\/1.596386","volume":"16","author":"TP Belikova","year":"1989","unstructured":"Belikova, T. P. & Yaroslavsky, L. P. Comments on \u201cImage feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography\u201d. Med. Phys. 16, 142. https:\/\/doi.org\/10.1118\/1.596386 (1989).","journal-title":"Med. Phys."},{"key":"19204_CR25","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","volume":"2","author":"KH Yu","year":"2018","unstructured":"Yu, K. H., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 719\u2013731. https:\/\/doi.org\/10.1038\/s41551-018-0305-z (2018).","journal-title":"Nat. Biomed. Eng."},{"key":"19204_CR26","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","volume":"71","author":"KW Johnson","year":"2018","unstructured":"Johnson, K. W. et al. Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 71, 2668\u20132679. https:\/\/doi.org\/10.1016\/j.jacc.2018.03.521 (2018).","journal-title":"J. Am. Coll. Cardiol."},{"key":"19204_CR27","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s00115-018-0557-6","volume":"89","author":"A Meyer-Lindenberg","year":"2018","unstructured":"Meyer-Lindenberg, A. Artificial intelligence in psychiatry-an overview. Der Nervenarzt 89, 861\u2013868. https:\/\/doi.org\/10.1007\/s00115-018-0557-6 (2018).","journal-title":"Der Nervenarzt"},{"key":"19204_CR28","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436\u2013444. https:\/\/doi.org\/10.1038\/nature14539 (2015).","journal-title":"Nature"},{"key":"19204_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","volume":"1213","author":"HP Chan","year":"2020","unstructured":"Chan, H. P., Samala, R. K., Hadjiiski, L. M. & Zhou, C. Deep learning in medical image analysis. Adv. Exp. Med. Biol. 1213, 3\u201321. https:\/\/doi.org\/10.1007\/978-3-030-33128-3_1 (2020).","journal-title":"Adv. Exp. Med. Biol."},{"key":"19204_CR30","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.20.24172","author":"AS Chaudhari","year":"2020","unstructured":"Chaudhari, A. S. et al. Diagnostic Accuracy of quantitative multi-contrast 5-minute knee MRI using prospective artificial intelligence image quality enhancement. AJR Am. J. Roentgenol. https:\/\/doi.org\/10.2214\/ajr.20.24172 (2020).","journal-title":"AJR Am. J. Roentgenol."},{"key":"19204_CR31","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1007\/s00330-019-06498-w","volume":"30","author":"M Schwyzer","year":"2020","unstructured":"Schwyzer, M. et al. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET\/CT: Impact of image reconstructions on diagnostic performance. Eur. Radiol. 30, 2031\u20132040. https:\/\/doi.org\/10.1007\/s00330-019-06498-w (2020).","journal-title":"Eur. Radiol."},{"key":"19204_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106369","volume":"210","author":"Y Eroglu","year":"2021","unstructured":"Eroglu, Y., Yildirim, K., \u00c7inar, A. & Yildirim, M. Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model. Comput. Methods Progr. Biomed. 210, 106369. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106369 (2021).","journal-title":"Comput. Methods Progr. Biomed."},{"key":"19204_CR33","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1002\/ima.22632","volume":"32","author":"Y Eroglu","year":"2022","unstructured":"Eroglu, Y., Yildirim, M. & Cinar, A. mRMR-based hybrid convolutional neural network model for classification of Alzheimer\u2019s disease on brain magnetic resonance images. Int. J. Imaging Syst. Technol. 32, 517\u2013527. https:\/\/doi.org\/10.1002\/ima.22632 (2022).","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"19204_CR34","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1118\/1.594709","volume":"7","author":"GN Hounsfield","year":"1980","unstructured":"Hounsfield, G. N. Computed medical imaging. Med. Phys. 7, 283\u2013290 (1980).","journal-title":"Med. Phys."},{"key":"19204_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/ima.22623","volume":"32","author":"M Yildirim","year":"2022","unstructured":"Yildirim, M. & Cinar, A. Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. Int. J. Imaging Syst. Technol. 32, 155\u2013162. https:\/\/doi.org\/10.1002\/ima.22623 (2022).","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"19204_CR36","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.artmed.2007.05.002","volume":"41","author":"SG Mougiakakou","year":"2007","unstructured":"Mougiakakou, S. G., Valavanis, I. K., Nikita, A. & Nikita, K. S. Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif. Intell. Med. 41, 25\u201337. https:\/\/doi.org\/10.1016\/j.artmed.2007.05.002 (2007).","journal-title":"Artif. Intell. Med."},{"key":"19204_CR37","doi-asserted-by":"publisher","first-page":"392","DOI":"10.2741\/4725","volume":"24","author":"M Biswas","year":"2019","unstructured":"Biswas, M. et al. State-of-the-art review on deep learning in medical imaging. Front. Biosci. (Landmark Edition) 24, 392\u2013426 (2019).","journal-title":"Front. Biosci. (Landmark Edition)"},{"key":"19204_CR38","doi-asserted-by":"publisher","unstructured":"Shen, W., Zhou, M., Yang, F., Yang, C. & Tian, J. Multi-scale convolutional neural networks for lung nodule classification. in Information Processing in Medical Imaging: Proceedings of the ... Conference, vol. 24, 588\u2013599. https:\/\/doi.org\/10.1007\/978-3-319-19992-4_46 (2015).","DOI":"10.1007\/978-3-319-19992-4_46"},{"key":"19204_CR39","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1109\/tmi.2016.2528120","volume":"35","author":"S Albarqouni","year":"2016","unstructured":"Albarqouni, S. et al. AggNet: Deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35, 1313\u20131321. https:\/\/doi.org\/10.1109\/tmi.2016.2528120 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19204_CR40","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.3390\/s19091992","volume":"19","author":"H Kutlu","year":"2019","unstructured":"Kutlu, H. & Avc\u0131, E. A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors (Basel, Switzerland) 19, 1992. https:\/\/doi.org\/10.3390\/s19091992 (2019).","journal-title":"Sensors (Basel, Switzerland)"},{"key":"19204_CR41","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1148\/radiol.2017170706","volume":"286","author":"K Yasaka","year":"2018","unstructured":"Yasaka, K., Akai, H., Abe, O. & Kiryu, S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: A preliminary study. Radiology 286, 887\u2013896. https:\/\/doi.org\/10.1148\/radiol.2017170706 (2018).","journal-title":"Radiology"},{"key":"19204_CR42","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s11277-018-5702-9","volume":"101","author":"A Mittal","year":"2018","unstructured":"Mittal, A., Hooda, R. & Sofat, S. LF-SegNet: A fully convolutional encoder\u2013decoder network for segmenting lung fields from chest radiographs. Wirel. Pers. Commun. 101, 511\u2013529. https:\/\/doi.org\/10.1007\/s11277-018-5702-9 (2018).","journal-title":"Wirel. Pers. Commun."},{"key":"19204_CR43","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/tpami.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A. & Cipolla, R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481\u20132495. https:\/\/doi.org\/10.1109\/tpami.2016.2644615 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19204_CR44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0193241","volume":"13","author":"K Edmunds","year":"2018","unstructured":"Edmunds, K. et al. Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities. PLoS ONE 13, e0193241. https:\/\/doi.org\/10.1371\/journal.pone.0193241 (2018).","journal-title":"PLoS ONE"},{"key":"19204_CR45","unstructured":"Gaidhani, B. R., Rajamenakshi, R. R. & Sonavane, S. in 2019 2nd International Conference on Intelligent Communication and Computational Techniques 242\u2013249 (IEEE Xplore, Manipal University Jaipur, 2019)."},{"key":"19204_CR46","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.3748\/wjg.v26.i25.3660","volume":"26","author":"SE Cao","year":"2020","unstructured":"Cao, S. E. et al. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World J. Gastroenterol. 26, 3660\u20133672. https:\/\/doi.org\/10.3748\/wjg.v26.i25.3660 (2020).","journal-title":"World J. Gastroenterol."},{"key":"19204_CR47","doi-asserted-by":"publisher","first-page":"96349","DOI":"10.1109\/ACCESS.2019.2929270","volume":"7","author":"K Xia","year":"2019","unstructured":"Xia, K., Yin, H., Qian, P., Jiang, Y. & Wang, S. Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7, 96349\u201396358. https:\/\/doi.org\/10.1109\/ACCESS.2019.2929270 (2019).","journal-title":"IEEE Access"},{"key":"19204_CR48","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1007\/s00330-019-06635-5","volume":"30","author":"LJ Oostveen","year":"2020","unstructured":"Oostveen, L. J. et al. Physical evaluation of an ultra-high-resolution CT scanner. Eur. Radiol. 30, 2552\u20132560. https:\/\/doi.org\/10.1007\/s00330-019-06635-5 (2020).","journal-title":"Eur. Radiol."},{"key":"19204_CR49","doi-asserted-by":"publisher","first-page":"361","DOI":"10.2214\/ajr.144.2.361","volume":"144","author":"NS Hadjis","year":"1985","unstructured":"Hadjis, N. S., Carr, D. H., Banks, L. & Pflug, J. J. The role of CT in the diagnosis of primary lymphedema of the lower limb. AJR Am. J. Roentgenol. 144, 361\u2013364. https:\/\/doi.org\/10.2214\/ajr.144.2.361 (1985).","journal-title":"AJR Am. J. Roentgenol."},{"key":"19204_CR50","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.apmr.2008.11.016","volume":"90","author":"A Sagen","year":"2009","unstructured":"Sagen, A., K\u00e5resen, R., Skaane, P. & Risberg, M. A. Validity for the simplified water displacement instrument to measure arm lymphedema as a result of breast cancer surgery. Arch. Phys. Med. Rehabil. 90, 803\u2013809. https:\/\/doi.org\/10.1016\/j.apmr.2008.11.016 (2009).","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"19204_CR51","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1111\/apha.12224","volume":"210","author":"J Aubrey","year":"2014","unstructured":"Aubrey, J. et al. Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta. Physiol. (Oxf.) 210, 489\u2013497. https:\/\/doi.org\/10.1111\/apha.12224 (2014).","journal-title":"Acta. Physiol. (Oxf.)"},{"key":"19204_CR52","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1002\/hbm.22083","volume":"34","author":"F Calamante","year":"2013","unstructured":"Calamante, F. et al. Super-resolution track-density imaging of thalamic substructures: Comparison with high-resolution anatomical magnetic resonance imaging at 7.0T. Hum. Brain Mapp. 34, 2538\u20132548. https:\/\/doi.org\/10.1002\/hbm.22083 (2013).","journal-title":"Hum. Brain Mapp."},{"key":"19204_CR53","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1002\/mrm.21570","volume":"59","author":"J Chen","year":"2008","unstructured":"Chen, J. et al. In vivo quantification of T1, T2, and apparent diffusion coefficient in the mouse retina at 11.74T. Magn. Reson. Med. 59, 731\u2013738. https:\/\/doi.org\/10.1002\/mrm.21570 (2008).","journal-title":"Magn. Reson. Med."},{"key":"19204_CR54","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1097\/01.prs.0000277995.25009.3e","volume":"120","author":"H Suami","year":"2007","unstructured":"Suami, H., Pan, W. R. & Taylor, G. I. Changes in the lymph structure of the upper limb after axillary dissection: Radiographic and anatomical study in a human cadaver. Plast. Reconstr. Surg. 120, 982\u2013991. https:\/\/doi.org\/10.1097\/01.prs.0000277995.25009.3e (2007).","journal-title":"Plast. Reconstr. Surg."},{"key":"19204_CR55","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s10549-013-2537-7","volume":"139","author":"KS Blum","year":"2013","unstructured":"Blum, K. S., Proulx, S. T., Luciani, P., Leroux, J. C. & Detmar, M. Dynamics of lymphatic regeneration and flow patterns after lymph node dissection. Breast Cancer Res. Treat. 139, 81\u201386. https:\/\/doi.org\/10.1007\/s10549-013-2537-7 (2013).","journal-title":"Breast Cancer Res. Treat."},{"key":"19204_CR56","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0106034","volume":"9","author":"S Kwon","year":"2014","unstructured":"Kwon, S., Agollah, G. D., Wu, G. & Sevick-Muraca, E. M. Spatio-temporal changes of lymphatic contractility and drainage patterns following lymphadenectomy in mice. PLoS ONE 9, e106034. https:\/\/doi.org\/10.1371\/journal.pone.0106034 (2014).","journal-title":"PLoS ONE"},{"key":"19204_CR57","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1089\/lrb.2009.7202","volume":"7","author":"SG Rockson","year":"2009","unstructured":"Rockson, S. G. The unique biology of lymphatic edema. Lymphat. Res. Biol. 7, 97\u2013100. https:\/\/doi.org\/10.1089\/lrb.2009.7202 (2009).","journal-title":"Lymphat. Res. Biol."},{"key":"19204_CR58","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1002\/(sici)1520-6300(1996)8:4<427::aid-ajhb2>3.0.co;2-v","volume":"8","author":"T Ohzeki","year":"1996","unstructured":"Ohzeki, T. et al. Fat areas on the extremities in normal weight and overweight children and adolescents: Comparison between age-related and weight-related changes in adiposity. Am. J. Hum. Biol. Off. J. Hum. Biol. Counc. 8, 427\u2013431. https:\/\/doi.org\/10.1002\/(sici)1520-6300(1996)8:4%3c427::aid-ajhb2%3e3.0.co;2-v (1996).","journal-title":"Am. J. Hum. Biol. Off. J. Hum. Biol. Counc."},{"key":"19204_CR59","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1089\/lrb.2015.0026","volume":"14","author":"M van Zanten","year":"2016","unstructured":"van Zanten, M., Piller, N. & Ward, L. C. Inter-changeability of impedance devices for lymphedema assessment. Lymphat. Res. Biol. 14, 88\u201394. https:\/\/doi.org\/10.1089\/lrb.2015.0026 (2016).","journal-title":"Lymphat. Res. Biol."},{"key":"19204_CR60","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1089\/lrb.2010.0020","volume":"9","author":"R Gaw","year":"2011","unstructured":"Gaw, R., Box, R. & Cornish, B. Bioimpedance in the assessment of unilateral lymphedema of a limb: The optimal frequency. Lymphat. Res. Biol. 9, 93\u201399. https:\/\/doi.org\/10.1089\/lrb.2010.0020 (2011).","journal-title":"Lymphat. Res. Biol."},{"key":"19204_CR61","first-page":"133","volume":"9","author":"AJ Grodzinsky","year":"1983","unstructured":"Grodzinsky, A. J. Electromechanical and physicochemical properties of connective tissue. Crit. Rev. Biomed. Eng. 9, 133\u2013199 (1983).","journal-title":"Crit. Rev. Biomed. Eng."},{"key":"19204_CR62","first-page":"203","volume":"47","author":"V Katch","year":"1975","unstructured":"Katch, V. & Weltman, A. Predictability of body segment volumes in living subjects. Hum. Biol. 47, 203\u2013218 (1975).","journal-title":"Hum. Biol."},{"key":"19204_CR63","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1136\/bmj.326.7400.1171","volume":"326","author":"H Melander","year":"2003","unstructured":"Melander, H., Ahlqvist-Rastad, J., Meijer, G. & Beermann, B. Evidence b(i)ased medicine\u2013selective reporting from studies sponsored by pharmaceutical industry: Review of studies in new drug applications. BMJ 326, 1171\u20131173. https:\/\/doi.org\/10.1136\/bmj.326.7400.1171 (2003).","journal-title":"BMJ"},{"key":"19204_CR64","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.1002\/bjs.10244","volume":"103","author":"JA Cook","year":"2016","unstructured":"Cook, J. A. & Ranstam, J. Overfitting. Br. J. Surg. 103, 1814. https:\/\/doi.org\/10.1002\/bjs.10244 (2016).","journal-title":"Br. J. Surg."},{"key":"19204_CR65","first-page":"53","volume":"63","author":"K Hattori","year":"1991","unstructured":"Hattori, K., Numata, N., Ikoma, M., Matsuzaka, A. & Danielson, R. R. Sex differences in the distribution of subcutaneous and internal fat. Hum. Biol. 63, 53\u201363 (1991).","journal-title":"Hum. Biol."},{"key":"19204_CR66","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/lrb.2008.1022","volume":"7","author":"H Brorson","year":"2009","unstructured":"Brorson, H., Ohlin, K., Olsson, G. & Karlsson, M. K. Breast cancer-related chronic arm lymphedema is associated with excess adipose and muscle tissue. Lymphat. Res. Biol. 7, 3\u201310. https:\/\/doi.org\/10.1089\/lrb.2008.1022 (2009).","journal-title":"Lymphat. Res. Biol."},{"issue":"Suppl 2","key":"19204_CR67","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10554-013-0332-5","volume":"29","author":"SU Shin","year":"2013","unstructured":"Shin, S. U. et al. Comparison of characteristic CT findings of lymphedema, cellulitis, and generalized edema in lower leg swelling. Int. J. Cardiovasc. Imaging 29(Suppl 2), 135\u2013143. https:\/\/doi.org\/10.1007\/s10554-013-0332-5 (2013).","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"19204_CR68","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/S0950-821X(05)80228-2","volume":"6","author":"R Haaverstad","year":"1992","unstructured":"Haaverstad, R., Nilsen, G., Myhre, H. O., Saether, O. D. & Rinck, P. A. The use of MRI in the investigation of leg oedema. Eur. J. Vasc. Surg. 6, 124\u2013129 (1992).","journal-title":"Eur. J. Vasc. Surg."},{"key":"19204_CR69","doi-asserted-by":"publisher","DOI":"10.3802\/jgo.2017.28.e18","volume":"28","author":"JS Yoo","year":"2017","unstructured":"Yoo, J. S. et al. Computed tomography-based quantitative assessment of lower extremity lymphedema following treatment for gynecologic cancer. J. Gynecol. Oncol. 28, e18. https:\/\/doi.org\/10.3802\/jgo.2017.28.e18 (2017).","journal-title":"J. Gynecol. Oncol."},{"key":"19204_CR70","doi-asserted-by":"publisher","first-page":"37","DOI":"10.22158\/wjssr.v3n1p37","volume":"3","author":"MA Bujang","year":"2016","unstructured":"Bujang, M. A. & Baharum, N. Sample size guideline for correlation analysis. World J. Soc. Sci. Res. 3, 37\u201346 (2016).","journal-title":"World J. Soc. Sci. Res."},{"key":"19204_CR71","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3969\/j.issn.1002-0829.2014.02.009","volume":"26","author":"C Feng","year":"2014","unstructured":"Feng, C. et al. Log-transformation and its implications for data analysis. Shanghai Arch. Psychiatry 26, 105\u2013109. https:\/\/doi.org\/10.3969\/j.issn.1002-0829.2014.02.009 (2014).","journal-title":"Shanghai Arch. Psychiatry"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-19204-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-19204-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-19204-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T07:10:06Z","timestamp":1663053006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-19204-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,13]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["19204"],"URL":"https:\/\/doi.org\/10.1038\/s41598-022-19204-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,13]]},"assertion":[{"value":"28 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15371"}}