{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:19:59Z","timestamp":1774523999669,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030466398","type":"print"},{"value":"9783030466404","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-46640-4_7","type":"book-chapter","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T23:24:36Z","timestamp":1589844276000},"page":"69-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes"],"prefix":"10.1007","author":[{"given":"Christian","family":"Lucas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda F.","family":"Aulmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Kemmling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Madany Mamlouk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattias P.","family":"Heinrich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1161\/STROKEAHA.118.023457","volume":"50","author":"G Broocks","year":"2019","unstructured":"Broocks, G., Rajput, F., Hanning, U., et al.: Highest lesion growth rates in patients with hyperacute stroke. Stroke 50(1), 189\u2013192 (2019)","journal-title":"Stroke"},{"issue":"3","key":"7_CR2","doi-asserted-by":"publisher","first-page":"277","DOI":"10.5853\/jos.2017.02054","volume":"19","author":"E-J Lee","year":"2017","unstructured":"Lee, E.-J., Kim, Y.-H., Kim, N., et al.: Deep into the brain: artificial intelligence in stroke imaging. J. Stroke 19(3), 277\u2013285 (2017)","journal-title":"J. Stroke"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"945","DOI":"10.3389\/fneur.2018.00945","volume":"9","author":"H Kamal","year":"2018","unstructured":"Kamal, H., Lopez, V., Sheth, S.A.: Machine learning in acute ischemic stroke neuroimaging. Front. Neurol. 9, 945 (2018)","journal-title":"Front. Neurol."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3389\/fneur.2018.00679","volume":"9","author":"S Winzeck","year":"2018","unstructured":"Winzeck, S., Hakim, A., McKinley, R., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)","journal-title":"Front. Neurol."},{"key":"7_CR5","unstructured":"Robben, D., Boers, A., Marquering, H., et al.: Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning. arXiv:1812.02496 (2018)"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.3389\/fneur.2018.01060","volume":"9","author":"J Pinto","year":"2018","unstructured":"Pinto, J., Mckinley, R., Alves, V., et al.: Stroke lesion outcome prediction based on MRI imaging combined with clinical information. Front. Neurol. 9, 1060 (2018)","journal-title":"Front. Neurol."},{"key":"7_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-030-11723-8_31","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"T Song","year":"2019","unstructured":"Song, T., Huang, N.: Integrated extractor, generator and segmentor for ischemic stroke lesion segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 310\u2013318. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_31"},{"key":"7_CR8","unstructured":"Foote, M.D., Zimmerman, B., Sawant, A., et al.: Real-time patient-specific lung radiotherapy targeting using deep learning. In: International Conference on Medical Imaging with Deep Learning (MIDL) (2018)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Rothberg, A., Jia, J., et al.: Integrating statistical prior knowledge into convolutional neural networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2017)","DOI":"10.1007\/978-3-319-66182-7_19"},{"key":"7_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-319-46454-1_4","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Yu","year":"2016","unstructured":"Yu, X., Zhou, F., Chandraker, M.: Deep deformation network for object landmark localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 52\u201370. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_4"},{"key":"7_CR11","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 28 (2015)"},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"989","DOI":"10.3389\/fneur.2018.00989","volume":"9","author":"C Lucas","year":"2018","unstructured":"Lucas, C., Kemmling, A., Bouteldja, N., et al.: Learning to predict ischemic stroke growth on acute CT perfusion data by interpolating low-dimensional shape representations. Front. Neurol. 9, 989 (2018)","journal-title":"Front. Neurol."},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Couronne, R., Louis, M., Durrleman, S.: Learning disease progression models with longitudinal data and missing values. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2019)","DOI":"10.1109\/ISBI.2019.8759198"},{"key":"7_CR14","unstructured":"Paszke, A., Gross, S., Chintala, S., et al.: Automatic differentiation in PyTorch. In: Advances in Neural Information Processing Systems (NIPS) - Autodiff Workshop (2017)"},{"key":"7_CR15","unstructured":"Sandk\u00fchler, R., Jud, C., Andermatt, S., et al.: AirLab: autograd image registration laboratory. arXiv:1806.09907 (2018)"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46640-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T22:03:10Z","timestamp":1747605790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46640-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030466398","9783030466404"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46640-4_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}