{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:10:30Z","timestamp":1760159430065,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032080080","type":"print"},{"value":"9783032080097","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:00:00Z","timestamp":1760227200000},"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\/978-3-032-08009-7_19","type":"book-chapter","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T04:36:48Z","timestamp":1760157408000},"page":"190-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Calibrated Self-supervised Vision Transformers Improve Intracranial Arterial Calcification Segmentation from\u00a0Clinical CT Head Scans"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2710-7248","authenticated-orcid":false,"given":"Benjamin","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2189-443X","authenticated-orcid":false,"given":"Grant","family":"Mair","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-6642","authenticated-orcid":false,"given":"Joanna M.","family":"Wardlaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2771-6546","authenticated-orcid":false,"given":"Maria del C.","family":"Vald\u00e9s Hern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,12]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","unstructured":"Agatston, A.S., et\u00a0al.: Quantification of coronary artery calcium using ultrafast computed tomography. J. Am. Coll. Cardiol. 15(4), 827\u2013832 (1990). https:\/\/doi.org\/10.1016\/0735-1097(90)90282-T","DOI":"10.1016\/0735-1097(90)90282-T"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Alajaji, S.A., et\u00a0al.: Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach. Oral Surg. Oral Med. Oral Pathol. Oral Radiol., S221244032300620X (2023). https:\/\/doi.org\/10.1016\/j.oooo.2023.08.009","DOI":"10.1016\/j.oooo.2023.08.009"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Banerjee, C., Chimowitz, M.I.: Stroke caused by atherosclerosis of the major intracranial arteries. Circ. Res. 120(3), 502\u2013513 (2017). https:\/\/doi.org\/10.1161\/CIRCRESAHA.116.308441","DOI":"10.1161\/CIRCRESAHA.116.308441"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Baxter, R., et\u00a0al.: The Scottish medical imaging archive: 57.3 million radiology studies linked to their medical records. Radiol. Artif. Intell. 6(1), e220266 (2024). https:\/\/doi.org\/10.1148\/ryai.220266","DOI":"10.1148\/ryai.220266"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Bortsova, G., et\u00a0al.: Segmentation of intracranial arterial calcification with deeply supervised residual dropout networks. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2017. Lecture Notes in Computer Science, vol. 10435, pp. 356\u2013364. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_41. https:\/\/link.springer.com\/10.1007\/978-3-319-66179-7_41","DOI":"10.1007\/978-3-319-66179-7_41"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Bortsova, G., et\u00a0al.: Automated segmentation and volume measurement of intracranial internal carotid artery calcification at noncontrast CT. Radiol. Artif. Intell. 3(5), e200226 (2021). https:\/\/doi.org\/10.1148\/ryai.2021200226","DOI":"10.1148\/ryai.2021200226"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Chen, Y.C., et al.: Correlation between intracranial arterial calcification and imaging of cerebral small vessel disease. Front. Neurol. 10, 426 (2019). https:\/\/doi.org\/10.3389\/fneur.2019.00426","DOI":"10.3389\/fneur.2019.00426"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale (2021). https:\/\/doi.org\/10.48550\/arXiv.2010.11929. arXiv:2010.11929 [cs]","DOI":"10.48550\/arXiv.2010.11929"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Feichtenhofer, C., et\u00a0al.: Masked autoencoders as spatiotemporal learners (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.09113. arXiv:2205.09113 [cs]","DOI":"10.48550\/arXiv.2205.09113"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., et\u00a0al.: UNETR: transformers for 3D medical image segmentation (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.10504. arXiv:2103.10504 [eess]","DOI":"10.48550\/arXiv.2103.10504"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., et\u00a0al.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images (2022). https:\/\/doi.org\/10.48550\/arXiv.2201.01266. arXiv:2201.01266 [eess]","DOI":"10.48550\/arXiv.2201.01266"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"He, K., et\u00a0al.: Masked autoencoders are scalable vision learners (2021). https:\/\/doi.org\/10.48550\/ARXIV.2111.06377. https:\/\/arxiv.org\/abs\/2111.06377","DOI":"10.48550\/ARXIV.2111.06377"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Isensee, F., et al.: nnU-Net revisited: a call for rigorous validation in 3D medical image segmentation. LNCS, vol. 15009, pp. 488\u2013498. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_47. https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_47","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"19_CR14","doi-asserted-by":"publisher","unstructured":"Isensee, F., et\u00a0al.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","DOI":"10.1038\/s41592-020-01008-z"},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Islam, S., et\u00a0al.: A comprehensive survey on applications of transformers for deep learning tasks (2023). https:\/\/doi.org\/10.48550\/arXiv.2306.07303. arXiv:2306.07303 [cs]","DOI":"10.48550\/arXiv.2306.07303"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Jin, B., Vald\u00e9s\u00a0Hern\u00e1ndez, M.D.C., Mair, G.: Large intracranial artery regions in MRI head templates for calcium segmentation (2024). https:\/\/doi.org\/10.7488\/DS\/7765","DOI":"10.7488\/DS\/7765"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Jin, B., et\u00a0al.: Pre-processing and quality control of large clinical CT head datasets for intracranial arterial calcification segmentation. In: Data Engineering in Medical Imaging, pp. 73\u201383. Springer, Cham (2025)","DOI":"10.1007\/978-3-031-73748-0_8"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Li, Y., et\u00a0al.: Exploring plain vision transformer backbones for object detection (2022). https:\/\/doi.org\/10.48550\/arXiv.2203.16527. arXiv:2203.16527 [cs]","DOI":"10.48550\/arXiv.2203.16527"},{"key":"19_CR19","doi-asserted-by":"publisher","unstructured":"Libby, P., et al.: Atherosclerosis. Nat. Rev. Dis. Primers. 5(1), 56 (2019). https:\/\/doi.org\/10.1038\/s41572-019-0106-z","DOI":"10.1038\/s41572-019-0106-z"},{"key":"19_CR20","doi-asserted-by":"publisher","unstructured":"Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill 1(10) (2016). https:\/\/doi.org\/10.23915\/distill.00003","DOI":"10.23915\/distill.00003"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation (2015). https:\/\/doi.org\/10.48550\/arXiv.1505.04597. arXiv:1505.04597 [cs]","DOI":"10.48550\/arXiv.1505.04597"},{"key":"19_CR22","doi-asserted-by":"publisher","unstructured":"Ryali, C., et\u00a0al.: Hiera: a hierarchical vision transformer without the bells-and-whistles (2023). https:\/\/doi.org\/10.48550\/ARXIV.2306.00989. https:\/\/arxiv.org\/abs\/2306.00989","DOI":"10.48550\/ARXIV.2306.00989"},{"key":"19_CR23","doi-asserted-by":"publisher","unstructured":"Subedi, D., et\u00a0al.: Intracranial carotid calcification on cranial computed tomography: visual scoring methods, semiautomated scores, and volume measurements in patients with stroke. Stroke 46(9), 2504\u20132509 (2015). https:\/\/doi.org\/10.1161\/STROKEAHA.115.009716","DOI":"10.1161\/STROKEAHA.115.009716"},{"key":"19_CR24","doi-asserted-by":"publisher","unstructured":"The IST-3 collaborative group: The benefits and harms of intravenous thrombolysis with recombinant tissue plasminogen activator within 6 h of acute ischaemic stroke (the third international stroke trial [IST-3]): a randomised controlled trial. Lancet 379(9834), 2352\u20132363 (2012). https:\/\/doi.org\/10.1016\/S0140-6736(12)60768-5","DOI":"10.1016\/S0140-6736(12)60768-5"},{"key":"19_CR25","doi-asserted-by":"publisher","unstructured":"The IST-3 collaborative group: Association between brain imaging signs, early and late outcomes, and response to intravenous alteplase after acute ischaemic stroke in the third international stroke trial (IST-3): secondary analysis of a randomised controlled trial. Lancet Neurol. 14(5), 485\u2013496 (2015). https:\/\/doi.org\/10.1016\/S1474-4422(15)00012-5","DOI":"10.1016\/S1474-4422(15)00012-5"},{"key":"19_CR26","doi-asserted-by":"publisher","unstructured":"Wald, T., et\u00a0al.: Primus: enforcing attention usage for 3d medical image segmentation (2025). https:\/\/doi.org\/10.48550\/arXiv.2503.01835. arXiv:2503.01835 [cs]","DOI":"10.48550\/arXiv.2503.01835"},{"key":"19_CR27","unstructured":"Zhang, C., et\u00a0al.: A survey on masked autoencoder for self-supervised learning in vision and beyond. arXiv preprint arXiv:2208.00173 (2022)"}],"container-title":["Lecture Notes in Computer Science","Data Engineering in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08009-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T04:36:51Z","timestamp":1760157411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08009-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,12]]},"ISBN":["9783032080080","9783032080097"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08009-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,12]]},"assertion":[{"value":"12 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of\u00a0Interests"}},{"value":"DEMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Data Engineering in Medical Imaging","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":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"demi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}