{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T06:07:49Z","timestamp":1783836469037,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234462","type":"print"},{"value":"9789819234479","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3447-9_27","type":"book-chapter","created":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T05:40:50Z","timestamp":1783834850000},"page":"330-340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HA-SAM3D: Hierarchical Adapter Enhanced SAM-Med3D for Drug-Resistant Focal Epilepsy Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Peixin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guixia","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Ingmar Bl\u00fcmcke, et al. The clinicopathologic spectrum of focal cortical dysplasias: A consensus classification proposed by an ad hoc task force of the ilae diagnostic methods commission 1 (2011)","DOI":"10.1111\/j.1528-1167.2010.02777.x"},{"issue":"2","key":"27_CR2","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1111\/j.1600-0404.2005.00555.x","volume":"113","author":"G Thomas Bast","year":"2006","unstructured":"Thomas Bast, G., Ramantani, A.S., Rating, D.: Focal cortical dysplasia: prevalence, clinical presentation and epilepsy in children and adults. Acta Neurol. Scand. 113(2), 72\u201381 (2006)","journal-title":"Acta Neurol. Scand."},{"issue":"3","key":"27_CR3","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s00234-021-02865-x","volume":"64","author":"H Urbach","year":"2022","unstructured":"Urbach, H., Kellner, E., Kremers, N., Bl\u00fcmcke, I., Demerath, T.: Mri of focal cortical dysplasia. Neuroradiology. 64(3), 443\u2013452 (2022)","journal-title":"Neuroradiology"},{"issue":"10","key":"27_CR4","doi-asserted-by":"publisher","first-page":"2844","DOI":"10.1093\/brain\/awr204","volume":"134","author":"J Wagner","year":"2011","unstructured":"Wagner, J., Weber, B., Urbach, H., Elger, C.E., Hans-J\u00fcrgenHuppertz.: Morphometric mri analysis improves detection of focal cortical dysplasia type ii. Brain. 134(10), 2844\u20132854 (2011)","journal-title":"Brain"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Sam-med3d: a vision foundation model for general-purpose segmentation on volumetric medical images. IEEE Trans. Neural Netw. Learn. Syst. (2025)","DOI":"10.1109\/TNNLS.2025.3586694"},{"issue":"17","key":"27_CR6","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1056\/NEJMoa1615335","volume":"377","author":"R Dwivedi","year":"2017","unstructured":"Dwivedi, R., et al.: Surgery for drug-resistant epilepsy in children. N. Engl. J. Med. 377(17), 1639\u20131647 (2017)","journal-title":"N. Engl. J. Med."},{"issue":"7","key":"27_CR7","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1111\/epi.12220","volume":"54","author":"I Bl\u00fcmcke","year":"2013","unstructured":"Bl\u00fcmcke, I., et al.: International consensus classification of hippocampal sclerosis in temporal lobe epilepsy: a task force report from the ilae commission on diagnostic methods. Epilepsia. 54(7), 1315\u20131329 (2013)","journal-title":"Epilepsia"},{"issue":"5","key":"27_CR8","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1111\/epi.17522","volume":"64","author":"L Walger","year":"2023","unstructured":"Walger, L., et al.: Artificial intelligence for the detection of focal cortical dysplasia: challenges in translating algorithms into clinical practice. Epilepsia. 64(5), 1093\u20131112 (2023)","journal-title":"Epilepsia"},{"issue":"1","key":"27_CR9","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13244-024-01635-6","volume":"15","author":"S Zhang","year":"2024","unstructured":"Zhang, S., Zhuang, Y., Luo, Y., Zhu, F., Zhao, W., Zeng, H.: Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia ii preoperative mri: a reliable approach. Insights Imaging. 15(1), 71 (2024)","journal-title":"Insights Imaging"},{"key":"27_CR10","first-page":"4015","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"A Kirillov","year":"2023","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"issue":"1","key":"27_CR11","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segmentanything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"27_CR12","first-page":"7132","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"J Hu","year":"2018","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)"},{"key":"27_CR13","first-page":"3","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV)","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)"},{"key":"27_CR14","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/3DV.2016.79","volume-title":"2016 Fourth International Conference on 3D Vision (3DV)","author":"F Milletari","year":"2016","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)"},{"key":"27_CR15","first-page":"562","volume-title":"Artificial Intelligence and Statistics","author":"C-Y Lee","year":"2015","unstructured":"Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., Zhuowen, T.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562\u2013570. PMLR (2015)"},{"issue":"2","key":"27_CR16","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.A., Petersen, J., MaierHein, 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":"9","key":"27_CR17","doi-asserted-by":"publisher","first-page":"3377","DOI":"10.1109\/TMI.2024.3398728","volume":"43","author":"A Shaker","year":"2024","unstructured":"Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M.H., Khan, F.S.: Unetr++: delving into efficient and accurate 3d medical image segmentation. IEEE Trans. Med. Imaging. 43(9), 3377\u20133390 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR18","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: enhancing long-range dependency forbiomedical image segmentation. arXiv preprint (2024). https:\/\/arxiv.org\/abs\/2401.04722"},{"key":"27_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103310","volume":"98","author":"C Chen","year":"2024","unstructured":"Chen, C., et al.: Ma-sam: modality-agnostic sam adaptation for 3d medical image segmentation. Med. Image Anal. 98, 103310 (2024)","journal-title":"Med. Image Anal."},{"key":"27_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547","volume":"102","author":"W Junde","year":"2025","unstructured":"Junde, W., et al.: Medical sam adapter: adapting segment anything model for medical image segmentation. Med. Image Anal. 102, 103547 (2025)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3447-9_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T05:40:51Z","timestamp":1783834851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3447-9_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"ISBN":["9789819234462","9789819234479"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3447-9_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]},"assertion":[{"value":"13 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}