{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:48:07Z","timestamp":1770817687722,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,10]]},"DOI":"10.1145\/3592686.3592745","type":"proceedings-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T23:21:27Z","timestamp":1685575287000},"page":"325-330","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Epicardial Adipose Tissue Segmentation and Quantification Based on Transformer Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8714-0900","authenticated-orcid":false,"given":"Junda","family":"Qu","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1268-0514","authenticated-orcid":false,"given":"Yuting","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine of Beijing Chaoyang Hospital, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6085-7416","authenticated-orcid":false,"given":"Miao","family":"He","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4348-6609","authenticated-orcid":false,"given":"Rongshen","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4437-558X","authenticated-orcid":false,"given":"Chunlin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9015-0541","authenticated-orcid":false,"given":"Minfu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine of Beijing Chaoyang Hospital, Capital Medical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4354-5591","authenticated-orcid":false,"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Capital Medical University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"#cr-split#-e_1_3_2_1_1_1.1","doi-asserted-by":"crossref","unstructured":"Le Jemtel T.H. Samson R. Ayinapudi K. Singh T. and Oparil S. 2019. Epicardial Adipose Tissue and Cardiovascular Disease. Curr. Hypertens. Rep. 21 5 (2019). DOI:https:\/\/doi.org\/10.1007\/s11906-019-0939-6. 10.1007\/s11906-019-0939-6","DOI":"10.1007\/s11906-019-0939-6"},{"key":"#cr-split#-e_1_3_2_1_1_1.2","doi-asserted-by":"crossref","unstructured":"Le Jemtel T.H. Samson R. Ayinapudi K. Singh T. and Oparil S. 2019. Epicardial Adipose Tissue and Cardiovascular Disease. Curr. Hypertens. Rep. 21 5 (2019). DOI:https:\/\/doi.org\/10.1007\/s11906-019-0939-6.","DOI":"10.1007\/s11906-019-0939-6"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20235989"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-022-00679-9"},{"key":"#cr-split#-e_1_3_2_1_4_1.1","doi-asserted-by":"crossref","unstructured":"Ansaldo A.M. Montecucco F. Sahebkar A. Dallegri F. and Carbone F. 2019. Epicardial adipose tissue and cardiovascular diseases. Int. J. Cardiol. 278 (2019) 254-260. DOI:https:\/\/doi.org\/10.1016\/j.ijcard.2018.09.089. 10.1016\/j.ijcard.2018.09.089","DOI":"10.1016\/j.ijcard.2018.09.089"},{"key":"#cr-split#-e_1_3_2_1_4_1.2","doi-asserted-by":"crossref","unstructured":"Ansaldo A.M. Montecucco F. Sahebkar A. Dallegri F. and Carbone F. 2019. Epicardial adipose tissue and cardiovascular diseases. Int. J. Cardiol. 278 (2019) 254-260. DOI:https:\/\/doi.org\/10.1016\/j.ijcard.2018.09.089.","DOI":"10.1016\/j.ijcard.2018.09.089"},{"key":"#cr-split#-e_1_3_2_1_5_1.1","doi-asserted-by":"crossref","unstructured":"Tarsitano M.G. Pandozzi C. Muscogiuri G. Sironi S. Pujia A. Lenzi A. and Giannetta E. 2022. Epicardial Adipose Tissue: A Novel Potential Imaging Marker of Comorbidities Caused by Chronic Inflammation. Nutrients. 14 14 (2022). DOI:https:\/\/doi.org\/10.3390\/nu14142926. 10.3390\/nu14142926","DOI":"10.3390\/nu14142926"},{"key":"#cr-split#-e_1_3_2_1_5_1.2","doi-asserted-by":"crossref","unstructured":"Tarsitano M.G. Pandozzi C. Muscogiuri G. Sironi S. Pujia A. Lenzi A. and Giannetta E. 2022. Epicardial Adipose Tissue: A Novel Potential Imaging Marker of Comorbidities Caused by Chronic Inflammation. Nutrients. 14 14 (2022). DOI:https:\/\/doi.org\/10.3390\/nu14142926.","DOI":"10.3390\/nu14142926"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.15829\/1560-4071-2022-4872"},{"key":"e_1_3_2_1_7_1","article-title":"Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning","volume":"12","author":"Ben\u010devi\u0107 M.","year":"2022","unstructured":"Ben\u010devi\u0107 , M. , Gali\u0107 , I. , Habijan , M. and Pi\u017eurica , A. 2022 . Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning : A Systematic Review. Appl. Sci. 12 , 10 (2022). DOI:https:\/\/doi.org\/10.3390\/app12105217. 10.3390\/app12105217 Ben\u010devi\u0107, M., Gali\u0107, I., Habijan, M. and Pi\u017eurica, A. 2022. Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review. Appl. Sci. 12, 10 (2022). DOI:https:\/\/doi.org\/10.3390\/app12105217.","journal-title":"A Systematic Review. Appl. Sci."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103424"},{"key":"e_1_3_2_1_9_1","first-page":"126","article-title":"Quantification of Epicardial Fat by Cardiac CT Imaging","volume":"4","author":"Coppini G.","year":"2011","unstructured":"Coppini , G. 2011 . Quantification of Epicardial Fat by Cardiac CT Imaging . Open Med. Inform. J. 4 , 1 (2011), 126 \u2013 135 . DOI:https:\/\/doi.org\/10.2174\/1874431101004010126. 10.2174\/1874431101004010126 Coppini, G. 2011. Quantification of Epicardial Fat by Cardiac CT Imaging. Open Med. Inform. J. 4, 1 (2011), 126\u2013135. DOI:https:\/\/doi.org\/10.2174\/1874431101004010126.","journal-title":"Open Med. Inform. J."},{"key":"e_1_3_2_1_10_1","volume-title":"Healthc. Eng. 2017, (2017","author":"Zlokolica V.","year":"2017","unstructured":"Zlokolica , V. , Krstanovi\u0107 , L. , Velicki , L. , Popovi\u0107 , B. , Janev , M. , Obradovi\u0107 , R. , Ralevi\u0107 , N.M. , Jovanov , L. and Babin , D . 2017. Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting. J . Healthc. Eng. 2017, (2017 ), 5817970. DOI:https:\/\/doi.org\/10.1155\/ 2017 \/5817970. 10.1155\/2017 Zlokolica, V., Krstanovi\u0107, L., Velicki, L., Popovi\u0107, B., Janev, M., Obradovi\u0107, R., Ralevi\u0107, N.M., Jovanov, L. and Babin, D. 2017. Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting. J. Healthc. Eng. 2017, (2017), 5817970. DOI:https:\/\/doi.org\/10.1155\/2017\/5817970."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4817577"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07482-5"},{"key":"e_1_3_2_1_13_1","volume-title":"ACM Int. Conf. Proceeding Ser.","author":"Li Z.","year":"2019","unstructured":"Li , Z. , Zou , L. and Yang , R . 2019. A neural network-based method for automatic pericardium segmentation . ACM Int. Conf. Proceeding Ser. ( 2019 ), 45\u201349. DOI:https:\/\/doi.org\/10.1145\/3339363.3339372. 10.1145\/3339363.3339372 Li, Z., Zou, L. and Yang, R. 2019. A neural network-based method for automatic pericardium segmentation. ACM Int. Conf. Proceeding Ser. (2019), 45\u201349. DOI:https:\/\/doi.org\/10.1145\/3339363.3339372."},{"key":"e_1_3_2_1_14_1","volume-title":"IEEE Access. XX, (2020","author":"Zhang Q.","year":"2020","unstructured":"Zhang , Q. , Zhou , J. , Zhang , B. , Member , S. and Jia , W . 2020. Automatic epicardial fat segmentation and quantification of CT scans using dual U-Nets with a morphological processing layer . IEEE Access. XX, (2020 ), 1\u201310. DOI:https:\/\/doi.org\/10.1109\/ACCESS. 2020 .3008190. 10.1109\/ACCESS.2020.3008190 Zhang, Q., Zhou, J., Zhang, B., Member, S. and Jia, W. 2020. Automatic epicardial fat segmentation and quantification of CT scans using dual U-Nets with a morphological processing layer. IEEE Access. XX, (2020), 1\u201310. DOI:https:\/\/doi.org\/10.1109\/ACCESS.2020.3008190."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2804799"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15965"},{"key":"e_1_3_2_1_17_1","first-page":"1","article-title":"Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans. Sci","volume":"12","author":"Hoori A.","year":"2022","unstructured":"Hoori , A. , Hu , T. , Lee , J. , Al-Kindi , S. , Rajagopalan , S. and Wilson , D.L. 2022 . Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans. Sci . Rep. 12 , 1 (2022), 1 \u2013 10 . DOI:https:\/\/doi.org\/10.1038\/s41598-022-06351-z. 10.1038\/s41598-022-06351-z Hoori, A., Hu, T., Lee, J., Al-Kindi, S., Rajagopalan, S. and Wilson, D.L. 2022. Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans. Sci. Rep. 12, 1 (2022), 1\u201310. DOI:https:\/\/doi.org\/10.1038\/s41598-022-06351-z.","journal-title":"Rep."},{"key":"e_1_3_2_1_18_1","volume-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Adv. Neural Inf. Process. Syst. 15, (2021","author":"Xie E.","year":"2077","unstructured":"Xie , E. , Wang , W. , Yu , Z. , Anandkumar , A. , Alvarez , J.M. and Luo , P . 2021 . SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Adv. Neural Inf. Process. Syst. 15, (2021 ), 1 2077 \u201312090. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P. 2021. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Adv. Neural Inf. Process. Syst. 15, (2021), 12077\u201312090."},{"key":"#cr-split#-e_1_3_2_1_19_1.1","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A. Nath V. Tang Y. Yang D. Roth H.R. and Xu D. 2022. Swin UNETR: Swin Transformers for\u00a0Semantic Segmentation of Brain Tumors in MRI Images. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 12962 LNCS (2022) 272-284. DOI:https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22. 10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"#cr-split#-e_1_3_2_1_19_1.2","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A. Nath V. Tang Y. Yang D. Roth H.R. and Xu D. 2022. Swin UNETR: Swin Transformers for\u00a0Semantic Segmentation of Brain Tumors in MRI Images. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 12962 LNCS (2022) 272-284. DOI:https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-022-10036-0"},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. IEEE Int. Conf. Comput. Vis. Iccv (2021","author":"Liu Z.","year":"2021","unstructured":"Liu , Z. , Lin , Y. , Cao , Y. , Hu , H. , Wei , Y. , Zhang , Z. , Lin , S. and Guo , B . 2021. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows . Proc. IEEE Int. Conf. Comput. Vis. Iccv (2021 ), 9992\u201310002. DOI:https:\/\/doi.org\/10.1109\/ICCV48922. 2021 .00986. 10.1109\/ICCV48922.2021.00986 Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S. and Guo, B. 2021. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proc. IEEE Int. Conf. Comput. Vis. Iccv (2021), 9992\u201310002. DOI:https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986."},{"key":"e_1_3_2_1_23_1","volume-title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision, 3DV","author":"Milletari F.","year":"2016","unstructured":"Milletari , F. , Navab , N. and Ahmadi , S . -A. 2016 . V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision, 3DV ( 2016 ), 565\u2013571. Milletari, F., Navab, N. and Ahmadi, S.-A. 2016. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision, 3DV (2016), 565\u2013571."}],"event":{"name":"BIC 2023: 2023 3rd International Conference on Bioinformatics and Intelligent Computing","location":"Sanya China","acronym":"BIC 2023"},"container-title":["Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592686.3592745","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3592686.3592745","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:07:46Z","timestamp":1750273666000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592686.3592745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":27,"alternative-id":["10.1145\/3592686.3592745","10.1145\/3592686"],"URL":"https:\/\/doi.org\/10.1145\/3592686.3592745","relation":{},"subject":[],"published":{"date-parts":[[2023,2,10]]},"assertion":[{"value":"2023-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}