{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:10:43Z","timestamp":1758359443230,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049834"},{"type":"electronic","value":"9783032049841"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04984-1_15","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:47Z","timestamp":1758299147000},"page":"149-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion-Based User-Guided Data Augmentation for\u00a0Coronary Stenosis Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8703-0322","authenticated-orcid":false,"given":"Sumin","family":"Seo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5554-808X","authenticated-orcid":false,"given":"In Kyu","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2740-0397","authenticated-orcid":false,"given":"Hyun-Woo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0007-0637","authenticated-orcid":false,"given":"Jaesik","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chung-Hwan","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, F.B.: Mortality in the United States\u2014provisional data, 2023. MMWR. Morbidity and Mortality Weekly Report, vol. 73 (2024)","DOI":"10.15585\/mmwr.mm7331a1"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Avram, R., et\u00a0al.: CathAI: fully automated coronary angiography interpretation and stenosis estimation. NPJ Digit. Med. 6(1), 142 (2023)","DOI":"10.1038\/s41746-023-00880-1"},{"issue":"2","key":"15_CR3","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0735-1097(88)90400-7","volume":"12","author":"KJ Beatt","year":"1988","unstructured":"Beatt, K.J., Luijten, H.E., de Feyter, P.J., van den Brand, M., Reiber, J.H., Serruys, P.W.: Change in diameter of coronary artery segments adjacent to stenosis after percutaneous transluminal coronary angioplasty: failure of percent diameter stenosis measurement to reflect morphologic changes induced by balloon dilation. J. Am. Coll. Cardiol. 12(2), 315\u2013323 (1988)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"2","key":"15_CR4","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1161\/01.CIR.55.2.329","volume":"55","author":"BG Brown","year":"1977","unstructured":"Brown, B.G., Bolson, E., Frimer, M., Dodge, H.T.: Quantitative coronary arteriography: estimation of dimensions, hemodynamic resistance, and atheroma mass of coronary artery lesions using the arteriogram and digital computation. Circulation 55(2), 329\u2013337 (1977)","journal-title":"Circulation"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Cobb, R., Cook, G.J.R., Reader, A.J.: Improved classification learning from highly imbalanced multi-label datasets of inflamed joints in [99mTc]maraciclatide imaging of arthritic patients by natural image and diffusion model augmentation. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15005, pp. 339\u2013348. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72086-4_32","DOI":"10.1007\/978-3-031-72086-4_32"},{"issue":"1","key":"15_CR6","doi-asserted-by":"publisher","first-page":"7582","DOI":"10.1038\/s41598-021-87174-2","volume":"11","author":"VV Danilov","year":"2021","unstructured":"Danilov, V.V., et al.: Real-time coronary artery stenosis detection based on modern neural networks. Sci. Rep. 11(1), 7582 (2021)","journal-title":"Sci. Rep."},{"issue":"1","key":"15_CR7","doi-asserted-by":"publisher","first-page":"11","DOI":"10.5334\/gh.1288","volume":"19","author":"M Di Cesare","year":"2024","unstructured":"Di Cesare, M., et al.: The heart of the world. Glob. Heart 19(1), 11 (2024)","journal-title":"Glob. Heart"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Fang, H., Han, B., Zhang, S., Zhou, S., Hu, C., Ye, W.M.: Data augmentation for object detection via controllable diffusion models. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1257\u20131266 (2024)","DOI":"10.1109\/WACV57701.2024.00129"},{"issue":"12","key":"15_CR9","doi-asserted-by":"publisher","first-page":"e13708","DOI":"10.1111\/exsy.13708","volume":"41","author":"A Jim\u00e9nez-Partinen","year":"2024","unstructured":"Jim\u00e9nez-Partinen, A., et al.: CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography. Expert. Syst. 41(12), e13708 (2024)","journal-title":"Expert. Syst."},{"key":"15_CR10","unstructured":"Khanam, R., Hussain, M.: YOLOv11: an overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725 (2024)"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"131945","DOI":"10.1016\/j.ijcard.2024.131945","volume":"405","author":"YI Kim","year":"2024","unstructured":"Kim, Y.I., et al.: Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning. Int. J. Cardiol. 405, 131945 (2024)","journal-title":"Int. J. Cardiol."},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Konz, N., Chen, Y., Dong, H., Mazurowski, M.A.: Anatomically-controllable medical image generation with segmentation-guided diffusion models. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15007, pp. 88\u201398. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72104-5_9","DOI":"10.1007\/978-3-031-72104-5_9"},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Li, P., Wang, S., Li, T., Lu, J., HuangFu, Y., Wang, D.: A large-scale CT and PET\/CT dataset for lung cancer diagnosis (version 5) [data set] (2020). https:\/\/doi.org\/10.7937\/TCIA.2020.NNC2-0461, https:\/\/www.cancerimagingarchive.net\/collection\/lung-pet-ct-dx\/","DOI":"10.7937\/TCIA.2020.NNC2-0461"},{"issue":"4","key":"15_CR14","doi-asserted-by":"publisher","first-page":"e256","DOI":"10.1016\/S2589-7500(22)00022-X","volume":"4","author":"A Lin","year":"2022","unstructured":"Lin, A., et al.: Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit. Health 4(4), e256\u2013e265 (2022)","journal-title":"Lancet Digit. Health"},{"key":"15_CR15","unstructured":"Lin, H., Liu, T., Katsaggelos, A., Kline, A.: StenUNet: automatic stenosis detection from x-ray coronary angiography. arXiv preprint arXiv:2310.14961 (2023)"},{"issue":"2","key":"15_CR16","doi-asserted-by":"publisher","first-page":"e21","DOI":"10.1016\/j.jacc.2021.09.006","volume":"79","author":"WC Members","year":"2022","unstructured":"Members, W.C., et al.: 2021 ACC\/AHA\/SCAI guideline for coronary artery revascularization: a report of the American College of Cardiology\/American heart association joint committee on clinical practice guidelines. J. Am. Coll. Cardiol. 79(2), e21\u2013e129 (2022)","journal-title":"J. Am. Coll. Cardiol."},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"105819","DOI":"10.1016\/j.cmpb.2020.105819","volume":"198","author":"JH Moon","year":"2021","unstructured":"Moon, J.H., et al.: Automatic stenosis recognition from coronary angiography using convolutional neural networks. Comput. Methods Programs Biomed. 198, 105819 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Oh, H.J., Jeong, W.K.: Controllable and efficient multi-class pathology nuclei data augmentation using text-conditioned diffusion models. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15004, pp. 36\u201346. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72083-3_4","DOI":"10.1007\/978-3-031-72083-3_4"},{"issue":"1","key":"15_CR19","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s41597-023-02102-5","volume":"10","author":"HH Pham","year":"2023","unstructured":"Pham, H.H., Nguyen, N.H., Tran, T.T., Nguyen, T.N., Nguyen, H.Q.: PediCXR: an open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children. Sci. Data 10(1), 240 (2023)","journal-title":"Sci. Data"},{"issue":"1","key":"15_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s41597-023-02871-z","volume":"11","author":"M Popov","year":"2024","unstructured":"Popov, M., et al.: Dataset for automatic region-based coronary artery disease diagnostics using X-ray angiography images. Sci. Data 11(1), 20 (2024)","journal-title":"Sci. Data"},{"issue":"1","key":"15_CR21","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","volume":"8","author":"D Ribli","year":"2018","unstructured":"Ribli, D., Horv\u00e1th, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 4165 (2018)","journal-title":"Sci. Rep."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Shivaie, S., Tohidi, H., Loganathan, P., Kar, M., Hashemy, H., Shafiee, M.A.: Interobserver variability of coronary stenosis characterized by coronary angiography: a single-center (toronto general hospital) retrospective chart review by staff cardiologists. Vasc. Health Risk Manage. 359\u2013368 (2024)","DOI":"10.2147\/VHRM.S431612"},{"issue":"1","key":"15_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s41747-022-00288-8","volume":"6","author":"MR Sunoqrot","year":"2022","unstructured":"Sunoqrot, M.R., Saha, A., Hosseinzadeh, M., Elschot, M., Huisman, H.: Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges. Eur. Radiol. Exp. 6(1), 35 (2022)","journal-title":"Eur. Radiol. Exp."},{"issue":"1","key":"15_CR25","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1038\/s41746-023-00773-3","volume":"6","author":"A Sylolypavan","year":"2023","unstructured":"Sylolypavan, A., Sleeman, D., Wu, H., Sim, M.: The impact of inconsistent human annotations on AI driven clinical decision making. NPJ Digit. Med. 6(1), 26 (2023)","journal-title":"NPJ Digit. Med."},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04984-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:52Z","timestamp":1758299152000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This study was supported by Medipixel, Inc. (Republic of Korea). The research was conducted using the company\u2019s proprietary software. The authors declare no additional competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}