{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:13:06Z","timestamp":1743156786828,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031811005"},{"type":"electronic","value":"9783031811012"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-81101-2_5","type":"book-chapter","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:23:11Z","timestamp":1738650191000},"page":"40-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved Stroke Lesion Segmentation via\u00a0Cross-Model Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Zixin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowen","family":"Pang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinru","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuyang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"issue":"9216","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1016\/S0140-6736(00)02237-6","volume":"355","author":"PA Barber","year":"2000","unstructured":"Barber, P.A., Demchuk, A.M., Zhang, J., Buchan, A.M.: Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. The Lancet 355(9216), 1670\u20131674 (2000)","journal-title":"The Lancet"},{"issue":"1","key":"5_CR2","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1038\/s41467-021-23492-3","volume":"12","author":"AK Bonkhoff","year":"2021","unstructured":"Bonkhoff, A.K., et al.: Outcome after acute ischemic stroke is linked to sex-specific lesion patterns. Nat. Commun. 12(1), 3289 (2021)","journal-title":"Nat. Commun."},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9https:\/\/github.com\/HuCaoFighting\/Swin-Unet","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"5_CR4","unstructured":"Cardoso, M.J., et\u00a0al.: MONAI: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022), https:\/\/github.com\/Project-MONAI\/MONAI"},{"key":"5_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117471","volume":"225","author":"S Cerri","year":"2021","unstructured":"Cerri, S., et al.: A contrast-adaptive method for simultaneous whole-brain and lesion segmentation in multiple sclerosis. Neuroimage 225, 117471 (2021)","journal-title":"Neuroimage"},{"key":"5_CR6","unstructured":"Chen, J., et al.: TransUnet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640\u20133649 (2016)","DOI":"10.1109\/CVPR.2016.396"},{"key":"5_CR8","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Galdran, A., Carneiro, G., Ballester, M.A.G.: On the optimal combination of cross-entropy and soft Dice losses for lesion segmentation with out-of-distribution robustness. In: Diabetic Foot Ulcers Grand Challenge, pp. 40\u201351. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-26354-5_4","DOI":"10.1007\/978-3-031-26354-5_4"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: MICCAI Brainlesion Workshop. pp. 272\u2013284. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"5_CR11","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"},{"issue":"1","key":"5_CR12","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1038\/s41597-022-01875-5","volume":"9","author":"MR Hernandez Petzsche","year":"2022","unstructured":"Hernandez Petzsche, M.R., et al.: ISLES 2022: a multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci. Data 9(1), 762 (2022)","journal-title":"Sci. Data"},{"key":"5_CR13","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"5_CR14","doi-asserted-by":"crossref","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). https:\/\/github.com\/MIC-DKFZ\/nnUNet","DOI":"10.1038\/s41592-020-01008-z"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: BossNAS: exploring hybrid CNN-Transformers with block-wisely self-supervised neural architecture search. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12281\u201312291 (2021)","DOI":"10.1109\/ICCV48922.2021.01206"},{"issue":"1","key":"5_CR16","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. Scientific Data 9(1), 320 (2022)","journal-title":"Scientific Data"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5_CR19","unstructured":"de\u00a0la Rosa, E., et\u00a0al.: A robust ensemble algorithm for ischemic stroke lesion segmentation: Generalizability and clinical utility beyond the isles challenge. arXiv preprint arXiv:2403.19425 (2024)"},{"issue":"2","key":"5_CR20","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1016\/j.neuroimage.2009.09.005","volume":"49","author":"N Shiee","year":"2010","unstructured":"Shiee, N., Bazin, P.L., Ozturk, A., Reich, D.S., Calabresi, P.A., Pham, D.L.: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49(2), 1524\u20131535 (2010)","journal-title":"Neuroimage"},{"key":"5_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-59719-1_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"B Shirokikh","year":"2020","unstructured":"Shirokikh, B., et al.: Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 523\u2013532. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_51"},{"key":"5_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-87193-2_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"W Wang","year":"2021","unstructured":"Wang, W., Chen, C., Ding, M., Yu, H., Zha, S., Li, J.: TransBTS: multimodal brain tumor segmentation using transformer. In: Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 109\u2013119. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_11"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, H.Y., et al.: nnFormer: volumetric medical image segmentation via a 3D Transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023), https:\/\/github.com\/282857341\/nnFormer","DOI":"10.1109\/TIP.2023.3293771"},{"key":"5_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"issue":"5","key":"5_CR25","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1161\/STROKEAHA.109.577023","volume":"41","author":"LL Zhu","year":"2010","unstructured":"Zhu, L.L., Lindenberg, R., Alexander, M.P., Schlaug, G.: Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. Stroke 41(5), 910\u2013915 (2010)","journal-title":"Stroke"}],"container-title":["Lecture Notes in Computer Science","Image Analysis in Stroke Diagnosis and Interventions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-81101-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:23:19Z","timestamp":1738650199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-81101-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031811005","9783031811012"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-81101-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"5 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SWITCH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stroke Workshop on Imaging and Treatment Challenges","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":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"switch2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/switchmiccai.github.io\/switch\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}