{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:20:07Z","timestamp":1770884407934,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Liaoning Province Science and Technology Plan Joint Program","award":["2023JH2\/101700369"],"award-info":[{"award-number":["2023JH2\/101700369"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant Nos. 82072008"],"award-info":[{"award-number":["Grant Nos. 82072008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenyang Science and Technology Bureau Public Health R&D Special Project","award":["22-321-32-02"],"award-info":[{"award-number":["22-321-32-02"]}]},{"name":"Application and Base Research Joint Program of Liaoning Province","award":["2022JH2\/101500024"],"award-info":[{"award-number":["2022JH2\/101500024"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2424010-19"],"award-info":[{"award-number":["N2424010-19"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Province Applied Basic Research Program","award":["2023JH2\/101300062"],"award-info":[{"award-number":["2023JH2\/101300062"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s40747-025-01861-5","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T10:12:33Z","timestamp":1745403153000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["M2FNet: multi-modality multi-level fusion network for segmentation of acute and sub-acute ischemic stroke"],"prefix":"10.1007","volume":"11","author":[{"given":"Shannan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xuanhe","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Ronghui","family":"Ju","sequence":"additional","affiliation":[]},{"given":"Peizhuo","family":"Zang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-1939","authenticated-orcid":false,"given":"Shouliang","family":"Qi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"1861_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102706","volume":"84","author":"P Ashtari","year":"2023","unstructured":"Ashtari P, Sima DM, De Lathauwer L, Sappey-Marinier D, Maes F, Van Huffel S (2023) Factorizer: a scalable interpretable approach to context modeling for medical image segmentation. Med Image Anal 84:102706. https:\/\/doi.org\/10.1016\/j.media.2022.102706","journal-title":"Med Image Anal"},{"key":"1861_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD007424.pub2","author":"M Brazzelli","year":"2009","unstructured":"Brazzelli M, Sandercock PA, Chappell FM, Celani MG, Righetti E, Arestis N, Wardlaw JM, Deeks JJ (2009) Magnetic resonance imaging versus computed tomography for detection of acute vascular lesions in patients presenting with stroke symptoms. Cochrane Database Syst Rev. https:\/\/doi.org\/10.1002\/14651858.CD007424.pub2","journal-title":"Cochrane Database Syst Rev"},{"key":"1861_CR3","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TMI.2021.3119385","volume":"41","author":"C Chen","year":"2022","unstructured":"Chen C, Dou Q, Jin Y, Liu Q, Heng PA (2022) Learning with privileged multimodal knowledge for unimodal segmentation. IEEE Trans Med Imaging 41:621\u2013632. https:\/\/doi.org\/10.1109\/TMI.2021.3119385","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR4","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.nicl.2017.06.016","volume":"15","author":"L Chen","year":"2017","unstructured":"Chen L, Bentley P, Rueckert D (2017) Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin 15:633\u2013643. https:\/\/doi.org\/10.1016\/j.nicl.2017.06.016","journal-title":"NeuroImage Clin"},{"key":"1861_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102311","volume":"76","author":"S Chen","year":"2022","unstructured":"Chen S, Sedghi Gamechi Z, Dubost F, van Tulder G, de Bruijne M (2022) An end-to-end approach to segmentation in medical images with CNN and posterior-CRF. Med Image Anal 76:102311. https:\/\/doi.org\/10.1016\/j.media.2021.102311","journal-title":"Med Image Anal"},{"key":"1861_CR6","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2016: 19th international conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19. Springer, pp 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"1861_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105521","volume":"194","author":"A Cl\u00e8rigues","year":"2020","unstructured":"Cl\u00e8rigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Llad\u00f3 X (2020) Acute and sub-acute stroke lesion segmentation from multimodal MRI. Comput Methods Programs Biomed 194:105521","journal-title":"Comput Methods Programs Biomed"},{"key":"1861_CR8","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"J Dolz","year":"2018","unstructured":"Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB (2018) HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans Med Imaging 38:1116\u20131126","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102561","volume":"112","author":"C Dong","year":"2024","unstructured":"Dong C, Dai D, Li Z, Xu S (2024) A novel deep network with triangular-star spatial\u2014spectral fusion encoding and entropy-aware double decoding for coronary artery segmentation. Inf Fusion 112:102561. https:\/\/doi.org\/10.1016\/j.inffus.2024.102561","journal-title":"Inf Fusion"},{"key":"1861_CR10","first-page":"20","volume":"19","author":"Z Dorjsembe","year":"2023","unstructured":"Dorjsembe Z, Pao H-K, Odonchimed S, Xiao F (2023) Conditional diffusion models for semantic 3D medical image synthesis. imaging 19:20","journal-title":"imaging"},{"key":"1861_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/978-3-030-87586-2_9","volume-title":"Machine learning in clinical neuroimaging, lecture notes in computer science","author":"W Duan","year":"2021","unstructured":"Duan W, Zhang L, Colman J, Gulli G, Ye X (2021) Multi-modal brain segmentation using hyper-fused convolutional neural network. In: Abdulkadir A, Kia SM, Habes M, Kumar V, Rondina JM, Tax C, Wolfers T (eds) Machine learning in clinical neuroimaging, lecture notes in computer science. Springer International Publishing, Cham, pp 82\u201391. https:\/\/doi.org\/10.1007\/978-3-030-87586-2_9"},{"key":"1861_CR12","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1179\/174313208X340987","volume":"30","author":"MG Dwyer","year":"2008","unstructured":"Dwyer MG, Bergsland N, Saluste E, Sharma J, Jaisani Z, Durfee J, Abdelrahman N, Minagar A, Hoque R, Munschauer FE (2008) Application of hidden Markov random field approach for quantification of perfusion\/diffusion mismatch in acute ischemic stroke. Neurol Res 30:827\u2013834","journal-title":"Neurol Res"},{"key":"1861_CR13","doi-asserted-by":"publisher","first-page":"e190217","DOI":"10.1148\/ryai.2020190217","volume":"2","author":"C Federau","year":"2020","unstructured":"Federau C, Christensen S, Scherrer N, Ospel JM, Schulze-Zachau V, Schmidt N, Breit H-C, Maclaren J, Lansberg M, Kozerke S (2020) Improved segmentation and detection sensitivity of diffusion-weighted stroke lesions with synthetically enhanced deep learning. Radiol Artif Intell 2:e190217. https:\/\/doi.org\/10.1148\/ryai.2020190217","journal-title":"Radiol Artif Intell"},{"key":"1861_CR14","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1007\/978-3-031-43901-8_74","volume-title":"Medical image computing and computer assisted intervention\u2014MICCAI 2023, lecture notes in computer science","author":"S G\u00f3mez","year":"2023","unstructured":"G\u00f3mez S, Mantilla D, Valenzuela B, Ortiz A, Vera D, Camacho P, Mart\u00ednez F (2023) Ischemic stroke segmentation from a cross-domain representation in multimodal diffusion studies. In: Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R (eds) Medical image computing and computer assisted intervention\u2014MICCAI 2023, lecture notes in computer science. Springer Nature Switzerland, Cham, pp 776\u2013785. https:\/\/doi.org\/10.1007\/978-3-031-43901-8_74"},{"key":"1861_CR15","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1016\/S0140-6736(16)00163-X","volume":"387","author":"M Goyal","year":"2016","unstructured":"Goyal M, Menon BK, Van Zwam WH, Dippel DWJ, Mitchell PJ, Demchuk AM, D\u00e1valos A, Majoie CBLM, Van Der Lugt A, De Miquel MA, Donnan GA, Roos YBWEM, Bonafe A, Jahan R, Diener H-C, Van Den Berg LA, Levy EI, Berkhemer OA, Pereira VM, Rempel J, Mill\u00e1n M, Davis SM, Roy D, Thornton J, Rom\u00e1n LS, Rib\u00f3 M, Beumer D, Stouch B, Brown S, Campbell BCV, Van Oostenbrugge RJ, Saver JL, Hill MD, Jovin TG (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723\u20131731. https:\/\/doi.org\/10.1016\/S0140-6736(16)00163-X","journal-title":"Lancet"},{"key":"1861_CR16","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2021) Swin unetr: swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI brainlesion workshop. Springer, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"1861_CR17","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. (WACV),Waikoloa, HI. pp 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"1861_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (CVPR),Las Vegas,\nNevada. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1861_CR19","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1097\/CCM.0000000000004597","volume":"48","author":"F Herpich","year":"2020","unstructured":"Herpich F, Rincon F (2020) Management of acute ischemic stroke. Crit Care Med 48:1654\u20131663. https:\/\/doi.org\/10.1097\/CCM.0000000000004597","journal-title":"Crit Care Med"},{"key":"1861_CR20","doi-asserted-by":"publisher","first-page":"5371","DOI":"10.1007\/s00330-022-08633-6","volume":"32","author":"C-J Juan","year":"2022","unstructured":"Juan C-J, Lin S-C, Li Y-H, Chang C-C, Jeng Y-H, Peng H-H, Huang T-Y, Chung H-W, Shen W-C, Tsai C-H, Chang R-F, Liu Y-J (2022) Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds. Eur Radiol 32:5371\u20135381. https:\/\/doi.org\/10.1007\/s00330-022-08633-6","journal-title":"Eur Radiol"},{"key":"1861_CR21","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, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61\u201378. https:\/\/doi.org\/10.1016\/j.media.2016.10.004","journal-title":"Med Image Anal"},{"key":"1861_CR22","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1111\/j.1747-4949.2012.00779.x","volume":"7","author":"D-W Kang","year":"2012","unstructured":"Kang D-W, Kwon JY, Kwon SU, Kim JS (2012) Wake-up or unclear-onset strokes: are they waking up to the world of thrombolysis therapy? Int J Stroke 7:311\u2013320. https:\/\/doi.org\/10.1111\/j.1747-4949.2012.00779.x","journal-title":"Int J Stroke"},{"key":"1861_CR23","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1161\/STROKEAHA.118.024261","volume":"50","author":"Y-C Kim","year":"2019","unstructured":"Kim Y-C, Lee J-E, Yu I, Song H-N, Baek I-Y, Seong J-K, Jeong H-G, Kim BJ, Nam HS, Chung J-W, Bang OY, Kim G-M, Seo W-K (2019) Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder\u2013decoder convolutional network. Stroke 50:1444\u20131451. https:\/\/doi.org\/10.1161\/STROKEAHA.118.024261","journal-title":"Stroke"},{"key":"1861_CR24","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.1109\/TMI.2024.3362879","volume":"43","author":"H Kuang","year":"2024","unstructured":"Kuang H, Wang Y, Liu J, Wang J, Cao Q, Hu B, Qiu W, Wang J (2024) Hybrid CNN-transformer network with circular feature interaction for acute ischemic stroke lesion segmentation on non-contrast CT scans. IEEE Trans Med Imaging 43:2303\u20132316. https:\/\/doi.org\/10.1109\/TMI.2024.3362879","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105524","volume":"193","author":"A Kumar","year":"2020","unstructured":"Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D (2020) CSNet: a new DeepNet framework for ischemic stroke lesion segmentation. Comput Methods Programs Biomed 193:105524. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105524","journal-title":"Comput Methods Programs Biomed"},{"key":"1861_CR26","unstructured":"Lee HH, Bao S, Huo Y, Landman BA (2022) 3D UX-Net: a large kernel volumetric ConvNet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076"},{"key":"1861_CR27","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1007\/s40747-022-00660-6","volume":"8","author":"D Li","year":"2022","unstructured":"Li D, Peng Y, Guo Y, Sun J (2022) TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation. Complex Intell Syst 8:2489\u20132505. https:\/\/doi.org\/10.1007\/s40747-022-00660-6","journal-title":"Complex Intell Syst"},{"key":"1861_CR28","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1109\/JBHI.2024.3353272","volume":"28","author":"W Li","year":"2024","unstructured":"Li W, Huang W, Zheng Y (2024) CorrDiff: corrective diffusion model for accurate MRI brain tumor segmentation. IEEE J Biomed Health Inform 28:1587\u20131598. https:\/\/doi.org\/10.1109\/JBHI.2024.3353272","journal-title":"IEEE J Biomed Health Inform"},{"key":"1861_CR29","doi-asserted-by":"crossref","unstructured":"Li Y, Shen L (2018) Deep learning based multimodal brain tumor diagnosis. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries: third international workshop, BrainLes 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers 3. Springer, pp 149\u2013158","DOI":"10.1007\/978-3-319-75238-9_13"},{"key":"1861_CR30","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/TMI.2023.3247814","volume":"42","author":"X Lin","year":"2023","unstructured":"Lin X, Yu L, Cheng K-T, Yan Z (2023) The lighter the better: rethinking transformers in medical image segmentation through adaptive pruning. IEEE Trans Med Imaging 42:2325\u20132337","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08872-8","author":"D Liu","year":"2023","unstructured":"Liu D, Sheng N, Han Y, Hou Y, Liu B, Zhang J, Zhang Q (2023) SCAU-net: 3D self-calibrated attention U-Net for brain tumor segmentation. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-08872-8","journal-title":"Neural Comput Appl"},{"key":"1861_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102135","volume":"72","author":"J Liu","year":"2021","unstructured":"Liu J, Liu H, Gong S, Tang Z, Xie Y, Yin H, Niyoyita JP (2021) Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure. Med Image Anal 72:102135","journal-title":"Med Image Anal"},{"key":"1861_CR33","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"1861_CR34","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","volume":"35","author":"O Maier","year":"2017","unstructured":"Maier O, Menze BH, Von der Gablentz J, H\u00e4ni L, Heinrich MP, Liebrand M, Winzeck S, Basit A, Bentley P, Chen L (2017) ISLES 2015\u2014a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 35:250\u2013269","journal-title":"Med Image Anal"},{"key":"1861_CR35","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D Vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"1861_CR36","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.mri.2022.06.001","volume":"92","author":"HS Moon","year":"2022","unstructured":"Moon HS, Heffron L, Mahzarnia A, Obeng-Gyasi B, Holbrook M, Badea CT, Feng W, Badea A (2022) Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images. Magn Reson Imaging 92:45\u201357. https:\/\/doi.org\/10.1016\/j.mri.2022.06.001","journal-title":"Magn Reson Imaging"},{"key":"1861_CR37","doi-asserted-by":"crossref","unstructured":"Myronenko A (2019) 3D MRI brain tumor segmentation using autoencoder regularization. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries: 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. Springer, pp 311\u2013320","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"1861_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2019.108575","volume":"333","author":"S Nazari-Farsani","year":"2020","unstructured":"Nazari-Farsani S, Nyman M, Karjalainen T, Bucci M, Isoj\u00e4rvi J, Nummenmaa L (2020) Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI. J Neurosci Methods 333:108575. https:\/\/doi.org\/10.1016\/j.jneumeth.2019.108575","journal-title":"J Neurosci Methods"},{"key":"1861_CR39","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"1861_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110471","volume":"153","author":"H Oliveira","year":"2024","unstructured":"Oliveira H, Gama PHT, Bloch I, Cesar RM (2024) Meta-learners for few-shot weakly-supervised medical image segmentation. Pattern Recogn 153:110471. https:\/\/doi.org\/10.1016\/j.patcog.2024.110471","journal-title":"Pattern Recogn"},{"key":"1861_CR41","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1590\/S0100-879X2009005000034","volume":"42","author":"MS Oliveira","year":"2009","unstructured":"Oliveira MS, Fernandes PT, Avelar WM, Santos SLM, Castellano G, Li LM (2009) Texture analysis of computed tomography images of acute ischemic stroke patients. Braz J Med Biol Res 42:1076\u20131079","journal-title":"Braz J Med Biol Res"},{"key":"1861_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102236","volume":"107","author":"Y Ou","year":"2023","unstructured":"Ou Y, Huang SX, Wong KK, Cummock J, Volpi J, Wang JZ, Wong STC (2023) BBox-Guided Segmentor: leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior. Comput Med Imaging Graph 107:102236. https:\/\/doi.org\/10.1016\/j.compmedimag.2023.102236","journal-title":"Comput Med Imaging Graph"},{"key":"1861_CR43","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/TMI.2023.3290149","volume":"42","author":"M \u00d6zbey","year":"2023","unstructured":"\u00d6zbey M, Dalmaz O, Dar SU, Bedel HA, \u00d6zturk \u015e, G\u00fcng\u00f6r A, \u00c7ukur T (2023) Unsupervised medical image translation with adversarial diffusion models. IEEE Trans Med Imaging 42:3524\u20133539","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR44","first-page":"24898","volume":"34","author":"B Pan","year":"2021","unstructured":"Pan B, Panda R, Jiang Y, Wang Z, Feris R, Oliva A (2021) IA-RED $^ 2$: interpretability-aware redundancy reduction for vision transformers. Adv Neural Inf Process Syst 34:24898\u201324911","journal-title":"Adv Neural Inf Process Syst"},{"key":"1861_CR45","unstructured":"Paszke A , Lerer A , Killeen T ,et al. PyTorch: an Imperative Style, high-performance deep learning library[C]. Advances in Neural Information Processing\nSystems 32, Volume 11 of 20: 32nd Conference on Neural Information Processing Systems\n(NeurIPS 2019). Vancouver(CA)"},{"key":"1861_CR46","unstructured":"Pechaud M, Jenkinson M, Smith S (2006) BET2-MRI-based estimation of brain, skull and scalp surfaces. FMRIB Technical Report TR06MP1 81\u201387"},{"key":"1861_CR47","doi-asserted-by":"publisher","first-page":"1640","DOI":"10.1109\/TMI.2023.3346285","volume":"43","author":"C Pei","year":"2023","unstructured":"Pei C, Wu F, Yang M, Pan L, Ding W, Dong J, Huang L, Zhuang X (2023) Multi-source domain adaptation for medical image segmentation. IEEE Trans Med Imaging 43:1640\u20131651","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103283","volume":"72","author":"M Platscher","year":"2022","unstructured":"Platscher M, Zopes J, Federau C (2022) Image translation for medical image generation: ischemic stroke lesion segmentation. Biomed Signal Process Control 72:103283. https:\/\/doi.org\/10.1016\/j.bspc.2021.103283","journal-title":"Biomed Signal Process Control"},{"key":"1861_CR49","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1861_CR50","first-page":"155","volume":"39","author":"M Saad","year":"2012","unstructured":"Saad M, Abu-Bakar SAR, Muda S, Mokji M, Abdullah AR (2012) Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI. IAENG Int J Comput Sci 39:155\u2013164","journal-title":"IAENG Int J Comput Sci"},{"key":"1861_CR51","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, Shahbaz Khan F (2024) UNETR++: delving into efficient and accurate 3D medical image segmentation. IEEE Trans Med Imaging 43:3377\u20133390. https:\/\/doi.org\/10.1109\/TMI.2024.3398728","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR52","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/TBME.2021.3087612","volume":"69","author":"T Shi","year":"2021","unstructured":"Shi T, Jiang H, Zheng B (2021) C 2 MA-Net: cross-modal cross-attention network for acute ischemic stroke lesion segmentation based on CT perfusion scans. IEEE Trans Biomed Eng 69:108\u2013118","journal-title":"IEEE Trans Biomed Eng"},{"key":"1861_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103250","volume":"97","author":"J Sun","year":"2024","unstructured":"Sun J, Liu Y, Xi Y, Coatrieux G, Coatrieux J-L, Ji X, Jiang L, Chen Y (2024) Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke. Med Image Anal 97:103250. https:\/\/doi.org\/10.1016\/j.media.2024.103250","journal-title":"Med Image Anal"},{"key":"1861_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110169","volume":"148","author":"Z Tang","year":"2024","unstructured":"Tang Z, Gao Y, Hui T, Peng F, Liu S (2024) MI 3 C: mining intra- and inter-image context for person search. Pattern Recogn 148:110169. https:\/\/doi.org\/10.1016\/j.patcog.2023.110169","journal-title":"Pattern Recogn"},{"key":"1861_CR55","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/JBHI.2021.3097591","volume":"26","author":"L Tomasetti","year":"2021","unstructured":"Tomasetti L, H\u00f8llesli LJ, Engan K, Kurz KD, Kurz MW, Khanmohammadi M (2021) Machine learning algorithms versus thresholding to segment ischemic regions in patients with acute ischemic stroke. IEEE J Biomed Health Inform 26:660\u2013672","journal-title":"IEEE J Biomed Health Inform"},{"key":"1861_CR56","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1161\/STROKEAHA.117.018858","volume":"49","author":"JP Tsai","year":"2018","unstructured":"Tsai JP, Mlynash M, Christensen S, Kemp S, Kim S, Mishra NK, Federau C, Nogueira RG, Jovin TG, Devlin TG (2018) Time from imaging to endovascular reperfusion predicts outcome in acute stroke. Stroke 49:952\u2013957","journal-title":"Stroke"},{"key":"1861_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43907-0_31","volume-title":"Unsupervised discovery of 3D hierarchical structure with generative diffusion features","author":"N Tursynbek","year":"2023","unstructured":"Tursynbek N, Niethammer M (2023) Unsupervised discovery of 3D hierarchical structure with generative diffusion features. Springer, Berlin"},{"key":"1861_CR58","doi-asserted-by":"crossref","unstructured":"Wang W, Chen C, Ding M, Yu H, Zha S, Li J (2021) Transbts: multimodal brain tumor segmentation using transformer. In: Medical image computing and computer assisted intervention\u2014MICCAI 2021: 24th international conference, Strasbourg, France, September 27\u2013October 1, 2021, proceedings, part I 24. Springer, pp 109\u2013119","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"1861_CR59","doi-asserted-by":"publisher","first-page":"2896","DOI":"10.1161\/STROKEAHA.121.037982","volume":"53","author":"KK Wong","year":"2022","unstructured":"Wong KK, Cummock JS, Li G, Ghosh R, Xu P, Volpi JJ, Wong STC (2022) Automatic segmentation in acute ischemic stroke: prognostic significance of topological stroke volumes on stroke outcome. Stroke 53:2896\u20132905. https:\/\/doi.org\/10.1161\/STROKEAHA.121.037982","journal-title":"Stroke"},{"key":"1861_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120637","volume":"230","author":"Z Wu","year":"2023","unstructured":"Wu Z, Zhang X, Li F, Wang S, Huang L, Li J (2023) W-Net: a boundary-enhanced segmentation network for stroke lesions. Expert Syst Appl 230:120637. https:\/\/doi.org\/10.1016\/j.eswa.2023.120637","journal-title":"Expert Syst Appl"},{"key":"1861_CR61","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-031-72111-3_54","volume-title":"Medical image computing and computer assisted intervention\u2014MICCAI 2024, lecture notes in computer science","author":"Z Xing","year":"2024","unstructured":"Xing Z, Ye T, Yang Y, Liu G, Zhu L (2024) SegMamba: long-range sequential modeling mamba for 3D medical image segmentation. In: Linguraru MG, Dou Q, Feragen A, Giannarou S, Glocker B, Lekadir K, Schnabel JA (eds) Medical image computing and computer assisted intervention\u2014MICCAI 2024, lecture notes in computer science. Springer Nature, Cham, pp 578\u2013588. https:\/\/doi.org\/10.1007\/978-3-031-72111-3_54"},{"key":"1861_CR62","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/JBHI.2023.3271808","volume":"27","author":"H Yang","year":"2023","unstructured":"Yang H, Zhou T, Zhou Y, Zhang Y, Fu H (2023) Flexible fusion network for multi-modal brain tumor segmentation. IEEE J Biomed Health Inform 27:3349\u20133359. https:\/\/doi.org\/10.1109\/JBHI.2023.3271808","journal-title":"IEEE J Biomed Health Inform"},{"key":"1861_CR63","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1109\/TNSE.2024.3524077","volume":"12","author":"Y Yang","year":"2025","unstructured":"Yang Y, Li G, Li D, Zhang J, Hu P, Hu L (2025) Integrating fuzzy clustering and graph convolution network to accurately identify clusters from attributed graph. IEEE Trans Netw Sci Eng 12:1112\u20131125. https:\/\/doi.org\/10.1109\/TNSE.2024.3524077","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"1861_CR64","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1109\/TFUZZ.2023.3338565","volume":"32","author":"Y Yang","year":"2024","unstructured":"Yang Y, Su X, Zhao B, Li G, Hu P, Zhang J, Hu L (2024) Fuzzy-based deep attributed graph clustering. IEEE Trans Fuzzy Syst 32:1951\u20131964. https:\/\/doi.org\/10.1109\/TFUZZ.2023.3338565","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1861_CR65","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1161\/STROKEAHA.111.639401","volume":"43","author":"AJ Yoo","year":"2012","unstructured":"Yoo AJ, Chaudhry ZA, Nogueira RG, Lev MH, Schaefer PW, Schwamm LH, Hirsch JA, Gonz\u00e1lez RG (2012) Infarct volume is a pivotal biomarker after intra-arterial stroke therapy. Stroke 43:1323\u20131330. https:\/\/doi.org\/10.1161\/STROKEAHA.111.639401","journal-title":"Stroke"},{"key":"1861_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120473","volume":"228","author":"C Yoon","year":"2023","unstructured":"Yoon C, Misra S, Kim K-J, Kim C, Kim BJ (2023) Collaborative multi-modal deep learning and radiomic features for classification of strokes within 6 h. Expert Syst Appl 228:120473. https:\/\/doi.org\/10.1016\/j.eswa.2023.120473","journal-title":"Expert Syst Appl"},{"key":"1861_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-022-3646-6","volume":"66","author":"H Yu","year":"2023","unstructured":"Yu H, Wu J (2023) A unified pruning framework for vision transformers. Sci China Inf Sci 66:1\u20132","journal-title":"Sci China Inf Sci"},{"key":"1861_CR68","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1109\/TMI.2018.2821244","volume":"37","author":"R Zhang","year":"2018","unstructured":"Zhang R, Zhao L, Lou W, Abrigo JM, Mok VC, Chu WC, Wang D, Shi L (2018) Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets. IEEE Trans Med Imaging 37:2149\u20132160","journal-title":"IEEE Trans Med Imaging"},{"key":"1861_CR69","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s12204-021-2273-9","volume":"27","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Liu S, Li C, Wang J (2022) Application of deep learning method on ischemic stroke lesion segmentation. J Shanghai Jiaotong Univ (Sci) 27:99\u2013111. https:\/\/doi.org\/10.1007\/s12204-021-2273-9","journal-title":"J Shanghai Jiaotong Univ (Sci)"},{"key":"1861_CR70","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yang J, Tian J, Shi Z, Zhong C, Zhang Y, He Z (2021) Modality-aware mutual learning for multi-modal medical image segmentation. In: Medical image computing and computer assisted intervention\u2014MICCAI 2021: 24th international conference, Strasbourg, France, September 27\u2013October 1, 2021, proceedings, part I 24. Springer, pp 589\u2013599","DOI":"10.1007\/978-3-030-87193-2_56"},{"key":"1861_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/3628179","volume":"2021","author":"B Zhao","year":"2021","unstructured":"Zhao B, Liu Z, Liu G, Cao C, Jin S, Wu H, Ding S (2021) Deep learning-based acute ischemic stroke lesion segmentation method on multimodal MR images using a few fully labeled subjects. Comput Math Methods Med 2021:1\u201313. https:\/\/doi.org\/10.1155\/2021\/3628179","journal-title":"Comput Math Methods Med"},{"key":"1861_CR72","unstructured":"Zhou H-Y, Guo J, Zhang Y, Yu L, Wang L, Yu Y (2021) nnformer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201"},{"key":"1861_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108417","volume":"124","author":"T Zhou","year":"2022","unstructured":"Zhou T, Ruan S, Vera P, Canu S (2022) A tri-attention fusion guided multi-modal segmentation network. Pattern Recogn 124:108417","journal-title":"Pattern Recogn"},{"key":"1861_CR74","volume-title":"Make-a-volume: leveraging latent diffusion models for cross-modality 3D brain MRI synthesis","author":"L Zhu","year":"2023","unstructured":"Zhu L, Xue Z, Jin Z, Liu X, He J, Liu Z, Yu L (2023) Make-a-volume: leveraging latent diffusion models for cross-modality 3D brain MRI synthesis. Springer, Berlin"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01861-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01861-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01861-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T11:23:39Z","timestamp":1747481019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01861-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,23]]},"references-count":74,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1861"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01861-5","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,23]]},"assertion":[{"value":"8 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted withe absence of any commercial or financial relationships that could be construed as potential conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"263"}}