{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:27:57Z","timestamp":1775737677101,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031787607","type":"print"},{"value":"9783031787614","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78761-4_11","type":"book-chapter","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T07:43:36Z","timestamp":1733557416000},"page":"113-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Segmenting Small Stroke Lesions with\u00a0Novel Labeling Strategies"],"prefix":"10.1007","author":[{"given":"Liang","family":"Shang","sequence":"first","affiliation":[]},{"given":"Zhengyang","family":"Lou","sequence":"additional","affiliation":[]},{"given":"Andrew L.","family":"Alexander","sequence":"additional","affiliation":[]},{"given":"Vivek","family":"Prabhakaran","sequence":"additional","affiliation":[]},{"given":"William A.","family":"Sethares","sequence":"additional","affiliation":[]},{"given":"Veena A.","family":"Nair","sequence":"additional","affiliation":[]},{"given":"Nagesh","family":"Adluru","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Borst, J., et al.: Effect of extended CT perfusion acquisition time on ischemic core and penumbra volume estimation in patients with acute ischemic stroke due to a large vessel occlusion. PLoS One 10(3), e0119409 (2015)","DOI":"10.1371\/journal.pone.0119409"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10571-016-0334-7","volume":"36","author":"RA Corriveau","year":"2016","unstructured":"Corriveau, R.A., et al.: The science of vascular contributions to cognitive impairment and dementia (VCID): a framework for advancing research priorities in the cerebrovascular biology of cognitive decline. Cell. Mol. Neurobiol. 36, 281\u2013288 (2016)","journal-title":"Cell. Mol. Neurobiol."},{"issue":"12","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1016\/S1474-4422(23)00277-6","volume":"22","author":"VL Feigin","year":"2023","unstructured":"Feigin, V.L., et al.: Pragmatic solutions to reduce the global burden of stroke: a world stroke organization-lancet neurology commission. Lancet Neurol. 22(12), 1160\u20131206 (2023)","journal-title":"Lancet Neurol."},{"key":"11_CR4","unstructured":"Huo, J., et al.: MAPPING: Model average with post-processing for stroke lesion segmentation. arXiv preprint arXiv:2211.15486 (2022)"},{"issue":"2","key":"11_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"16","key":"11_CR6","doi-asserted-by":"publisher","first-page":"4669","DOI":"10.1002\/hbm.24729","volume":"40","author":"KL Ito","year":"2019","unstructured":"Ito, K.L., Kim, H., Liew, S.L.: A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data. Hum. Brain Mapp. 40(16), 4669\u20134685 (2019)","journal-title":"Hum. Brain Mapp."},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"issue":"1","key":"11_CR8","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","volume":"9","author":"SL Liew","year":"2022","unstructured":"Liew, S.L., et al.: A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Sci. Data 9(1), 320 (2022)","journal-title":"Sci. Data"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"11_CR10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/s43856-021-00062-8","volume":"1","author":"CF Liu","year":"2021","unstructured":"Liu, C.F., et al.: Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke. Commun. Med. 1(1), 61 (2021)","journal-title":"Commun. Med."},{"key":"11_CR11","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0104011","volume":"9","author":"JZ Tsai","year":"2014","unstructured":"Tsai, J.Z., et al.: Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction. PLoS ONE 9(8), e104011 (2014)","journal-title":"PLoS ONE"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Wardlaw, J.M., et\u00a0al.: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12(8), 822\u2013838 (2013)","DOI":"10.1016\/S1474-4422(13)70124-8"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Wong, A., et al.: Small lesion segmentation in brain MRIs with subpixel embedding. In: International MICCAI Brainlesion Workshop, pp. 75\u201387. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_6"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Xu, B., et al.: Orchestral fully convolutional networks for small lesion segmentation in brain MRI. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 889\u2013892. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363714"},{"key":"11_CR17","first-page":"1","volume":"2021","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Xu, S., Tan, L., Wang, H., Meng, J.: Stroke lesion detection and analysis in MRI images based on deep learning. J. Healthc. Eng. 2021, 1\u20139 (2021)","journal-title":"J. Healthc. Eng."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78761-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T08:06:30Z","timestamp":1733558790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78761-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"ISBN":["9783031787607","9783031787614"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78761-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"6 December 2024","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 Interests"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}