{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:38:57Z","timestamp":1777045137695,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031986932","type":"print"},{"value":"9783031986949","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-98694-9_23","type":"book-chapter","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T05:51:56Z","timestamp":1752472316000},"page":"317-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structurally Different Neural Network Blocks for\u00a0the\u00a0Segmentation of\u00a0Atrial and\u00a0Aortic Perivascular Adipose Tissue in\u00a0Multi-centre CT Angiography Scans"],"prefix":"10.1007","author":[{"given":"Ikboljon","family":"Sobirov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Siddique","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parijat","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Halborg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos P.","family":"Kotanidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zarqaish","family":"Fatima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry","family":"West","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheena","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Lyasheva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donna","family":"Alexander","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Adlam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Das","family":"Indrajeet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aparna","family":"Deshpande","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amrita","family":"Bajaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan C. L.","family":"Rodrigues","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin J.","family":"Hudson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivek","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Krasopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rana","family":"Sayeed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pete","family":"Tomlins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheerag","family":"Shirodaria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keith M.","family":"Channon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Neubauer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charalambos","family":"Antoniades","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Yaqub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Akoumianakis, I., et\u00a0al.: Adipose tissue\u2013derived wnt5a regulates vascular redox signaling in obesity via usp17\/rac1-mediated activation of nadph oxidases. Science translational medicine 11(510), eaav5055 (2019)","DOI":"10.1126\/scitranslmed.aav5055"},{"issue":"10444","key":"23_CR2","doi-asserted-by":"publisher","first-page":"2606","DOI":"10.1016\/S0140-6736(24)00596-8","volume":"403","author":"K Chan","year":"2024","unstructured":"Chan, K., et al.: Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the orfan multicentre, longitudinal cohort study. Lancet 403(10444), 2606\u20132618 (2024)","journal-title":"Lancet"},{"key":"23_CR3","unstructured":"Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"23_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"issue":"1","key":"23_CR5","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1038\/s41597-023-02016-2","volume":"10","author":"R Gharleghi","year":"2023","unstructured":"Gharleghi, R., Adikari, D., Ellenberger, K., Webster, M., Ellis, C., Sowmya, A., Ooi, S., Beier, S.: Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries. Sci. Data 10(1), 128 (2023)","journal-title":"Sci. Data"},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"102049","DOI":"10.1016\/j.compmedimag.2022.102049","volume":"97","author":"R Gharleghi","year":"2022","unstructured":"Gharleghi, R., Adikari, D., Ellenberger, K., Ooi, S.Y., Ellis, C., Chen, C.M., Gao, R., He, Y., Hussain, R., Lee, C.Y., et al.: Automated segmentation of normal and diseased coronary arteries-the asoca challenge. Comput. Med. Imaging Graph. 97, 102049 (2022)","journal-title":"Comput. Med. Imaging Graph."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et\u00a0al.: Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, pp. 272\u2013284. Springer (2022)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Iantsen, A., Jaouen, V., Visvikis, D., Hatt, M.: Squeeze-and-excitation normalization for brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Part II 6, pp. 366\u2013373. Springer (2021)","DOI":"10.1007\/978-3-030-72087-2_32"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual u-net. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges: 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, pp. 371\u2013380. Springer (2019)","DOI":"10.1007\/978-3-030-12029-0_40"},{"issue":"10","key":"23_CR11","doi-asserted-by":"publisher","first-page":"e705","DOI":"10.1016\/S2589-7500(22)00132-7","volume":"4","author":"CP Kotanidis","year":"2022","unstructured":"Kotanidis, C.P., et al.: Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine ct angiograms: a prospective outcomes validation study in covid-19. Lancet Digital Health 4(10), e705\u2013e716 (2022)","journal-title":"Lancet Digital Health"},{"key":"23_CR12","unstructured":"Lee, H.H., et\u00a0al.: 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076 (2022)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3d mri brain tumor segmentation using autoencoder regularization. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Part II 4. pp. 311\u2013320. Springer (2019)","DOI":"10.1007\/978-3-030-11726-9_28"},{"issue":"43","key":"23_CR15","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1093\/eurheartj\/ehz592","volume":"40","author":"EK Oikonomou","year":"2019","unstructured":"Oikonomou, E.K., et al.: A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary ct angiography. Eur. Heart J. 40(43), 3529\u20133543 (2019)","journal-title":"Eur. Heart J."},{"issue":"4","key":"23_CR16","first-page":"519","volume":"2","author":"F Otsuka","year":"2013","unstructured":"Otsuka, F., Yahagi, K., Sakakura, K., Virmani, R.: Why is the mammary artery so special and what protects it from atherosclerosis? Ann. Cardiothoracic Surg. 2(4), 519 (2013)","journal-title":"Ann. Cardiothoracic Surg."},{"key":"23_CR17","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. In: Breakthroughs in Statistics: Methodology and Distribution, pp. 196\u2013202. Springer (1992)","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Yu, X., et\u00a0al.: Unest: Local spatial representation learning with hierarchical transformer for efficient medical segmentation. arXiv preprint arXiv:2209.14378 (2022)","DOI":"10.1016\/j.media.2023.102939"},{"key":"23_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-030-87193-2_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Liu, H., Hu, Q.: TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 14\u201324. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_2"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-98694-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:13:27Z","timestamp":1777040007000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-98694-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,15]]},"ISBN":["9783031986932","9783031986949"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-98694-9_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,15]]},"assertion":[{"value":"15 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leeds","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.leeds.ac.uk\/miua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}