{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:28:11Z","timestamp":1766068091594,"version":"3.35.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10489-024-06085-7","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T04:37:15Z","timestamp":1735879035000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent mask image reconstruction for cardiac image segmentation through local\u2013global fusion"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5829-5944","authenticated-orcid":false,"given":"Assia","family":"Boukhamla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabiha","family":"Azizi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samir Brahim","family":"Belhaouari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"6085_CR1","doi-asserted-by":"crossref","unstructured":"Mall PK et al (2023) A comprehensive review of deep neural networks for medical image processing: recent developments and future opportunities. Healthc Anal 4","DOI":"10.1016\/j.health.2023.100216"},{"key":"6085_CR2","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neunet.2020.03.007","volume":"126","author":"MB Calisto","year":"2020","unstructured":"Calisto MB, Lai-Yuen KS (2020) AdaEn-Net: an ensemble of adaptive 2D\u20133D fully convolutional networks for medical image segmentation. Neural Netw 126:76\u201394. https:\/\/doi.org\/10.1016\/j.neunet.2020.03.007","journal-title":"Neural Netw"},{"issue":"3","key":"6085_CR3","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JBHI.2018.2852635","volume":"23","author":"A Chakravarty","year":"2019","unstructured":"Chakravarty A, Sivaswamy J (2019) RACE-Net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform 23(3):1151\u20131162. https:\/\/doi.org\/10.1109\/JBHI.2018.2852635","journal-title":"IEEE J Biomed Health Inform"},{"key":"6085_CR4","doi-asserted-by":"publisher","unstructured":"Boukhamla A et al (2023) GANs investigation for multimodal medical data interpretation: basic architectures and overview. In: 2023 International Conference on Control, Automation and Diagnosis (ICCAD), pp 01\u201306. https:\/\/doi.org\/10.1109\/ICCAD57653.2023.10152386.","DOI":"10.1109\/ICCAD57653.2023.10152386"},{"issue":"6","key":"6085_CR5","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TRPMS.2023.3265863","volume":"7","author":"P-H Conze","year":"2023","unstructured":"Conze P-H, Andrade-Miranda G, Singh VK, Jaouen V, Visvikis D (2023) Current and emerging trends in medical image segmentation with deep learning. IEEE Trans Radiat and Plasma Med Sci 7(6):545\u2013569. https:\/\/doi.org\/10.1109\/TRPMS.2023.3265863","journal-title":"IEEE Trans Radiat and Plasma Med Sci"},{"key":"6085_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104791","volume":"84","author":"H Xiao","year":"2023","unstructured":"Xiao H, Li L, Liu Q, Zhu X, Zhang Q (2023) Transformers in medical image segmentation: a review. Biomed Signal Process Control 84. https:\/\/doi.org\/10.1016\/j.bspc.2023.104791","journal-title":"Biomed Signal Process Control"},{"key":"6085_CR7","doi-asserted-by":"publisher","unstructured":"Wang Z, Zheng J-Q, Voiculescu I (2022) An uncertainty-aware transformer for MRI cardiac semantic segmentation via mean teachers. In: Yang G, Aviles-Rivero A, Roberts M, and Sch\u00f6nlieb CB (eds) Medical Image Understanding and Analysis (MIUA 2022). lecture Notes in Computer Science. Springer International Publishing, Cham, pp 494\u2013507. https:\/\/doi.org\/10.1007\/978-3-031-12053-4_37.","DOI":"10.1007\/978-3-031-12053-4_37"},{"key":"6085_CR8","doi-asserted-by":"publisher","first-page":"129763","DOI":"10.1109\/ACCESS.2023.3302522","volume":"11","author":"C Fan","year":"2023","unstructured":"Fan C, Su Q, Xiao Z, Su H, Hou A, Luan B (2023) ViT-FRD: a vision transformer model for cardiac mri image segmentation based on feature recombination distillation. IEEE Access 11:129763\u2013129772. https:\/\/doi.org\/10.1109\/ACCESS.2023.3302522","journal-title":"IEEE Access"},{"key":"6085_CR9","doi-asserted-by":"publisher","unstructured":"Azad R et al (2024) Advances in medical image analysis with vision transformers: a comprehensive review. Med Image Anal 91. https:\/\/doi.org\/10.1016\/j.media.2023.103000","DOI":"10.1016\/j.media.2023.103000"},{"key":"6085_CR10","unstructured":"Chen J,\u00a0Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y\u00a0(2021) TransUNet: transformers make strong encoders for medical image segmentation. Available at\u00a0http:\/\/arxiv.org\/abs\/2102.04306. Accessed 3 Mar 2023"},{"key":"6085_CR11","doi-asserted-by":"publisher","unstructured":"Hatamizadeh A, Tang Y,\u00a0Nath V,\u00a0Yang D,\u00a0Myronenko A,\u00a0Landman B,\u00a0Roth HR,\u00a0Xu D (2022) UNETR: transformers for 3d medical image segmentation. In: 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp 1748\u20131758. https:\/\/doi.org\/10.1109\/WACV51458.2022.00181","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"6085_CR12","doi-asserted-by":"publisher","unstructured":"Cao H,\u00a0Wang Y,\u00a0Chen J,\u00a0Jiang D,\u00a0Zhang X,\u00a0Tian Q,\u00a0Wang M (2023) Swin-Unet: unet-like pure transformer for medical image segmentation. In: Karlinsky L, Michaeli T, Nishino K (eds) Computer Vision \u2013 ECCV 2022 Workshops. Lecture notes in computer science. Springer Nature Switzerland, Cham, pp 205\u2013218. https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"6085_CR13","doi-asserted-by":"publisher","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2022) Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi A, Bakas S (eds) Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer International Publishing, Cham, pp 272\u2013284. https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22.","DOI":"10.1007\/978-3-031-08999-2_22"},{"issue":"3","key":"6085_CR14","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1080\/01969722.2022.2162736","volume":"55","author":"LB Ammar","year":"2024","unstructured":"Ammar LB, Gasmi K, Ltaifa IB (2024) ViT-TB: ensemble learning based ViT model for tuberculosis recognition. Cybern Syst 55(3):634\u2013653. https:\/\/doi.org\/10.1080\/01969722.2022.2162736","journal-title":"Cybern Syst"},{"key":"6085_CR15","doi-asserted-by":"publisher","unstructured":"Qiu J,\u00a0Mitra J,\u00a0Ghose S,\u00a0Dumas C,\u00a0Yang J,\u00a0Sarachan B, Judson MA (2024) A multichannel CT and radiomics-guided CNN-ViT (RadCT-CNNViT) ensemble network for diagnosis of pulmonary sarcoidosis. Diagnostics 14(10):1049.\u00a0https:\/\/doi.org\/10.3390\/diagnostics14101049.","DOI":"10.3390\/diagnostics14101049"},{"key":"6085_CR16","doi-asserted-by":"publisher","unstructured":"Xu G, Wu X, Zhang X, and He X (2021) LeViT-UNet: make faster encoders with transformer for medical image segmentation.\u00a0arXiv:  https:\/\/doi.org\/10.48550\/arXiv.2107.08623\n. Accessed 3 Mar 2023.","DOI":"10.48550\/arXiv.2107.08623"},{"key":"6085_CR17","doi-asserted-by":"publisher","unstructured":"Graham B\u00a0et al (2021) LeViT: a vision transformer in ConvNet\u2019s clothing for faster inference.\u00a0arXiv:\u00a0https:\/\/doi.org\/10.48550\/arXiv.2104.01136.","DOI":"10.48550\/arXiv.2104.01136"},{"key":"6085_CR18","doi-asserted-by":"publisher","unstructured":"Yang X, Tian X (2022) TransNUNet: using attention mechanism for whole heart segmentation. In: 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), pp 553\u2013556. https:\/\/doi.org\/10.1109\/ICPECA53709.2022.9719101","DOI":"10.1109\/ICPECA53709.2022.9719101"},{"key":"6085_CR19","doi-asserted-by":"publisher","unstructured":"\u00a0Gao Y, Zhou M, and Metaxas DN (2021) UTNet: a hybrid transformer architecture for medical image segmentation,\u00a0in medical image computing and computer assisted intervention \u2013 MICCAI. In: de Bruijne M, PC, Cattin PC, Cotin S,Padoy N, Speidel S, Zheng Y, and Essert C (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 61\u201371. https:\/\/doi.org\/10.1007\/978-3-030-87199-46.","DOI":"10.1007\/978-3-030-87199-46"},{"key":"6085_CR20","doi-asserted-by":"publisher","unstructured":"Gao Y, Zhou M, Liu D, Yan Z, Zhang S, and Metaxas DN (2023) A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark.\u00a0arXiv:  https:\/\/doi.org\/10.48550\/arXiv.2203.00131. Accessed 01 Aug 2023.","DOI":"10.48550\/arXiv.2203.00131"},{"key":"6085_CR21","doi-asserted-by":"publisher","unstructured":"Deng K et al (2021) TransBridge: a lightweight transformer for left ventricle segmentation in echocardiography, in simplifying medical ultrasound. In: Noble JA, Aylward S, Grimwood A, Min Z, Lee S-L, and Hu\u00a0Y (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 63\u201372. https:\/\/doi.org\/10.1007\/978-3-030-87583-1_7.","DOI":"10.1007\/978-3-030-87583-1_7"},{"key":"6085_CR22","doi-asserted-by":"publisher","unstructured":"Wu Y et al (2022) D-Former: a U-shaped dilated transformer for 3D medical image segmentation.\u00a0arXiv: https:\/\/doi.org\/10.48550\/arXiv.2201.00462.","DOI":"10.48550\/arXiv.2201.00462"},{"key":"6085_CR23","doi-asserted-by":"publisher","unstructured":"Aghapanah H et al (2024) CardSegNet: an adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI. Comput Med Imaging Graph 115. https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102382","DOI":"10.1016\/j.compmedimag.2024.102382"},{"key":"6085_CR24","doi-asserted-by":"publisher","unstructured":"Huang X, Deng Z, Li D, and Yuan X (2021) MISSFormer: an effective medical image segmentation transformer.\u00a0arXiv:https:\/\/doi.org\/10.48550\/arXiv.2109.07162.","DOI":"10.48550\/arXiv.2109.07162"},{"key":"6085_CR25","doi-asserted-by":"publisher","unstructured":"Zhou H-Y, Guo J, Zhang Y, Yu L, Wang L, and Yu Y (2022) nnFormer: interleaved transformer for volumetric segmentation.\u00a0arXiv:https:\/\/doi.org\/10.48550\/arXiv.2109.03201.","DOI":"10.48550\/arXiv.2109.03201"},{"key":"6085_CR26","doi-asserted-by":"publisher","unstructured":"Liu D\u00a0et al (2022) TransFusion: multi-view divergent fusion for medical image segmentation with transformers,\u201d in medical image computing and computer assisted intervention \u2013 MICCAI. In: Wang L, Dou Q, Fletcher PT, Speidel S, and Li S (eds) in lecture notes in computer science. Cham: Springer Nature Switzerland, pp. 485\u2013495. https:\/\/doi.org\/10.1007\/978-3-031-16443-9_47.","DOI":"10.1007\/978-3-031-16443-9_47"},{"key":"6085_CR27","doi-asserted-by":"publisher","unstructured":"Ji Y\u00a0et al (2021) Multi-compound Transformer for Accurate Biomedical Image Segmentation, in medical image computing and computer assisted intervention \u2013 MICCAI. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, and Essert C (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 326\u2013336. https:\/\/doi.org\/10.1007\/978-3-030-87193-2_31.","DOI":"10.1007\/978-3-030-87193-2_31"},{"issue":"3","key":"6085_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13908","volume":"24","author":"B Li","year":"2023","unstructured":"Li B, Yang T, Zhao X (2023) NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation. J Appl Clin Med Phys 24(3). https:\/\/doi.org\/10.1002\/acm2.13908","journal-title":"J Appl Clin Med Phys"},{"issue":"1","key":"6085_CR29","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1049\/ipr2.12936","volume":"18","author":"R Yang","year":"2024","unstructured":"Yang R, Liu K, Liang Y (2024) A fusion-attention swin transformer for cardiac MRI image segmentation. IET Image Proc 18(1):105\u2013115. https:\/\/doi.org\/10.1049\/ipr2.12936","journal-title":"IET Image Proc"},{"key":"6085_CR30","doi-asserted-by":"publisher","unstructured":"Luo X, Hu M, Song S, Wang G, and Zhang S (2021) Semi-supervised medical image segmentation via cross teaching between CNN and transformer. arXiv: https:\/\/doi.org\/10.48550\/arXiv.2112.04894.\u00a0Accessed 02 Aug. 2023","DOI":"10.48550\/arXiv.2112.04894"},{"key":"6085_CR31","doi-asserted-by":"publisher","unstructured":"Mazher M et al (2024) Self-supervised spatial\u2013temporal transformer fusion based federated framework for 4D cardiovascular image segmentation. Inf Fusion 106. https:\/\/doi.org\/10.1016\/j.inffus.2024.102256","DOI":"10.1016\/j.inffus.2024.102256"},{"key":"6085_CR32","doi-asserted-by":"publisher","unstructured":"Zhou T, Cheng Q, Lu H, Li Q, Zhang X, Qiu S (2023) Deep learning methods for medical image fusion: a review. Comput Biol Med 160. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106959","DOI":"10.1016\/j.compbiomed.2023.106959"},{"key":"6085_CR33","doi-asserted-by":"publisher","unstructured":"Hermessi H, Mourali O, Zagrouba E (2021) Multimodal medical image fusion review: theoretical background and recent advances. Signal Process 183. https:\/\/doi.org\/10.1016\/j.sigpro.2021.108036","DOI":"10.1016\/j.sigpro.2021.108036"},{"key":"6085_CR34","doi-asserted-by":"publisher","unstructured":"\u00a0Sahu A, Bhateja V, Krishn A, and Himanshi (2014) Medical image fusion with laplacian pyramids, in 2014 international conference on medical imaging, m-health and emerging communication systems(MedCom),\u00a0pp. 448\u2013453. https:\/\/doi.org\/10.1109\/MedCom.2014.7006050","DOI":"10.1109\/MedCom.2014.7006050"},{"key":"6085_CR35","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.procs.2015.10.057","volume":"70","author":"V Bhavana","year":"2015","unstructured":"Bhavana V, Krishnappa HK (2015) Multi-modality medical image fusion using discrete wavelet transform. Procedia Comput Sci 70:625\u2013631.https:\/\/doi.org\/10.1016\/j.procs.2015.10.057","journal-title":"Procedia Comput Sci"},{"key":"6085_CR36","unstructured":"Tang L, Li L, Qian J, Zhang J, Pan J-S (2016) NSCT-based multimodal medical image fusion with sparse representation and pulse coupled neural network.\u00a0J Inf Hiding Multim Signal Process 7(6):1306\u20131316"},{"issue":"12","key":"6085_CR37","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1109\/TBME.2013.2282461","volume":"60","author":"S Das","year":"2013","unstructured":"Das S, Kundu MK (2013) A neuro-fuzzy approach for medical image fusion. IEEE Trans Biomed Eng 60(12):3347\u20133353.https:\/\/doi.org\/10.1109\/TBME.2013.2282461","journal-title":"IEEE Trans Biomed Eng"},{"key":"6085_CR38","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.dsp.2018.04.002","volume":"79","author":"HR Shahdoosti","year":"2018","unstructured":"Shahdoosti HR, Mehrabi A (2018) Multimodal image fusion using sparse representation classification in tetrolet domain. Digital Signal Process 79:9\u201322. https:\/\/doi.org\/10.1016\/j.dsp.2018.04.002","journal-title":"Digital Signal Process"},{"key":"6085_CR39","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1016\/j.ijleo.2018.12.028","volume":"182","author":"O Prakash","year":"2019","unstructured":"Prakash O, Park CM, Khare A, Jeon M, Gwak J (2019) Multiscale fusion of multimodal medical images using lifting scheme based biorthogonal wavelet transform. Optik 182:995\u20131014. https:\/\/doi.org\/10.1016\/j.ijleo.2018.12.028","journal-title":"Optik"},{"key":"6085_CR40","doi-asserted-by":"publisher","unstructured":"Singh S et al (2023) A review of image fusion: methods, applications and performance metrics. Digital Signal Process 137. https:\/\/doi.org\/10.1016\/j.dsp.2023.104020","DOI":"10.1016\/j.dsp.2023.104020"},{"issue":"1","key":"6085_CR41","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S1566-2535(01)00037-9","volume":"3","author":"S Li","year":"2002","unstructured":"Li S, Kwok JT, Wang Y (2002) Using the discrete wavelet frame transform to merge landsat TM and SPOT panchromatic images. Inf Fusion 3(1):17\u201323. https:\/\/doi.org\/10.1016\/S1566-2535(01)00037-9","journal-title":"Inf Fusion"},{"key":"6085_CR42","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, and Brox T (2015) U-Net: convolutional networks for biomedical image segmentation, in medical image computing and computer-assisted intervention \u2013 MICCAI. In:\u00a0Navab N, Hornegger J, Wells WM, and Frangi AF (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6085_CR43","doi-asserted-by":"publisher","unstructured":"Gao L, Zhang L, Liu C, Wu S (2020) Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif Intell Med 108. https:\/\/doi.org\/10.1016\/j.artmed.2020.101935","DOI":"10.1016\/j.artmed.2020.101935"},{"key":"6085_CR44","doi-asserted-by":"publisher","unstructured":"Simonyan K and Zisserman A (2015) Very Deep convolutional networks for large-scale image recognition.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1409.1556, Accessed 18 March 2022. [Online].","DOI":"10.48550\/arXiv.1409.1556"},{"key":"6085_CR45","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, and Luo P (2021) \u201cSegFormer: Simple and efficient design for semantic segmentation with transformers,\u201d in advances in neural information processing systems, Curran Associates, Inc. pp. 12077\u201312090. Accessed: 6 Aug 2023. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/64f1f27bf1b4ec22924fd0acb550c235-Abstract.html"},{"key":"6085_CR46","doi-asserted-by":"crossref","unstructured":"Chaoyang Z, Shibao S, Wenmao H, Pengcheng Z (2024) FDR-TransUNet: a novel encoder-decoder architecture with vision transformer for improved medical image segmentation. Comput Biol Med 169","DOI":"10.1016\/j.compbiomed.2023.107858"},{"issue":"1","key":"6085_CR47","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1186\/s12859-023-05409-7","volume":"24","author":"Y Chong","year":"2023","unstructured":"Chong Y, Xie N, Liu X, Pan S (2023) P-TransUNet: an improved parallel network for medical image segmentation. BMC Bioinformatics 24(1):285. https:\/\/doi.org\/10.1186\/s12859-023-05409-7","journal-title":"BMC Bioinformatics"},{"key":"6085_CR48","doi-asserted-by":"crossref","unstructured":"\u00a0Liu Z\u00a0et al (2023)\u00a0\u201cSwin transformer: hierarchical vision transformer using shifted windows,\u201d presented at the proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022. Accessed 9 Aug. 2023. [Online]. Available: https:\/\/openaccess.thecvf.com\/content\/ICCV2021\/html\/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"6085_CR49","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, and Ahmadi S-A (2016) \u201cV-Net: fully convolutional neural networks for volumetric medical image segmentation,\u201d in 2016 fourth international conference on 3D Vision (3DV), pp. 565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"issue":"11","key":"6085_CR50","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O et al (2018) Deep learning techniques for automatic MRI Cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525.https:\/\/doi.org\/10.1109\/TMI.2018.2837502","journal-title":"IEEE Trans Med Imaging"},{"key":"6085_CR51","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, and Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database, In 2009 IEEE conference on computer vision and pattern recognition, pp. 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6085_CR52","doi-asserted-by":"publisher","unstructured":"Xu S and Quan H (2021) ECT-NAS: searching efficient CNN-Transformers architecture for medical image segmentation, In 2021 IEEE international conference on bioinformatics and biomedicine (BIBM), pp. 1601\u20131604. https:\/\/doi.org\/10.1109\/BIBM52615.2021.9669734","DOI":"10.1109\/BIBM52615.2021.9669734"},{"key":"6085_CR53","doi-asserted-by":"publisher","unstructured":"Chen Y, Lu X, Xie Q (2023) ATFormer: Advanced transformer for medical image segmentation. Biomed Signal Process Control 85. https:\/\/doi.org\/10.1016\/j.bspc.2023.105079","DOI":"10.1016\/j.bspc.2023.105079"},{"key":"6085_CR54","doi-asserted-by":"publisher","unstructured":"Li J et al (2023) MCRformer: morphological constraint reticular transformer for 3D medical image segmentation. Expert Syst Appl 232. https:\/\/doi.org\/10.1016\/j.eswa.2023.120877","DOI":"10.1016\/j.eswa.2023.120877"},{"key":"6085_CR55","doi-asserted-by":"publisher","unstructured":"Isensee F, Jaeger PF, Full PM, Wolf I,Engelhardt S, and Maier-Hein MH, \u201cautomatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features,\u201d in statistical atlases and computational models of the heart.ACDC and MMWHS challenges. In: Pop M, Sermesant M, Jodoin P-M, Lalande A, Zhuang X, Yang G, Young A, and Bernard O (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 120\u2013129. https:\/\/doi.org\/10.1007\/978-3-319-75541-0_13.","DOI":"10.1007\/978-3-319-75541-0_13"},{"key":"6085_CR56","doi-asserted-by":"publisher","unstructured":"Baumgartner CF, Koch LM, Pollefeys M, and Konukoglu E (2018) \u201cAn exploration of 2D and 3D deep learning techniques for cardiac MR Image segmentation,\u201d in statistical atlases and computational models of the heart. ACDC and MMWHS challenges. In:\u00a0Pop M, Sermesant M, Jodoin P-M, Lalande A, Zhuang X, Yang G, Young A, and Bernard O (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 111\u2013119. https:\/\/doi.org\/10.1007\/978-3-319-75541-0_12","DOI":"10.1007\/978-3-319-75541-0_12"},{"issue":"3","key":"6085_CR57","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/JBHI.2018.2865450","volume":"23","author":"C Zotti","year":"2019","unstructured":"Zotti C, Luo Z, Lalande A, Jodoin P-M (2019) Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE J Biomed Health Inform 23(3):1119\u20131128. https:\/\/doi.org\/10.1109\/JBHI.2018.2865450","journal-title":"IEEE J Biomed Health Inform"},{"key":"6085_CR58","doi-asserted-by":"publisher","unstructured":"Painchaud N, Skandarani Y, Judge T, Bernard O, A. Lalande A, and Jodoin P-M (2019) Cardiac MRI segmentation with strong anatomical guarantees, in medical image computing and computer assisted intervention \u2013 MICCAI. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap PT, and A. Khan A (eds) in lecture notes in computer science. Cham: Springer International Publishing, pp. 632\u2013640. https:\/\/doi.org\/10.1007\/978-3-030-32245-8_70","DOI":"10.1007\/978-3-030-32245-8_70"},{"key":"6085_CR59","doi-asserted-by":"publisher","unstructured":"Khened M, Kollerathu VA, Krishnamurthi G (2019) Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med Image Anal 51:21\u201345. https:\/\/doi.org\/10.1016\/j.media.2018.10.004","DOI":"10.1016\/j.media.2018.10.004"},{"issue":"6","key":"6085_CR60","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1109\/JSTSP.2020.3013351","volume":"14","author":"G Simantiris","year":"2020","unstructured":"Simantiris G, Tziritas G (2020) Cardiac MRI segmentation with a dilated CNN incorporating domain-specific constraints. IEEE J Sel Topics Signal Process 14(6):1235\u20131243. https:\/\/doi.org\/10.1109\/JSTSP.2020.3013351","journal-title":"IEEE J Sel Topics Signal Process"},{"key":"6085_CR61","doi-asserted-by":"publisher","unstructured":"da Silva IFS, Silva AC, de Paiva AC, Gattass M (2022) A cascade approach for automatic segmentation of cardiac structures in short-axis cine-MR images using deep neural networks. Expert Syst Appl 197.https:\/\/doi.org\/10.1016\/j.eswa.2022.116704","DOI":"10.1016\/j.eswa.2022.116704"},{"key":"6085_CR62","doi-asserted-by":"publisher","unstructured":"Dong S et al (2022) DeU-Net 2.0: Enhanced deformable U-Net for 3D cardiac cine MRI segmentation. Med Image Anal 78. https:\/\/doi.org\/10.1016\/j.media.2022.102389","DOI":"10.1016\/j.media.2022.102389"},{"key":"6085_CR63","doi-asserted-by":"publisher","unstructured":"Wang K-N et al (2022) AWSnet: an auto-weighted supervision attention network for myocardial scar and edema segmentation in multi-sequence cardiac magnetic resonance images. Med Image Anal 77. https:\/\/doi.org\/10.1016\/j.media.2022.102362","DOI":"10.1016\/j.media.2022.102362"},{"key":"6085_CR64","doi-asserted-by":"publisher","unstructured":"Kim D, Kim J (2023) Vision transformer compression and architecture exploration with efficient embedding space search. In: Wang L, Gall J, Chin T-J, Sato I, Chellappa R (eds) computer vision \u2013 ACCV 2022. Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, pp 524\u2013540. https:\/\/doi.org\/10.1007\/978-3-031-26313-2_32","DOI":"10.1007\/978-3-031-26313-2_32"},{"key":"6085_CR65","doi-asserted-by":"publisher","unstructured":"Alqahtani A, Xie X, and Jones MW (2021) Literature review of deep network compression.\u00a0Informatics 8(4).\u00a0https:\/\/doi.org\/10.3390\/informatics8040077","DOI":"10.3390\/informatics8040077"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06085-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06085-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06085-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T14:53:56Z","timestamp":1738335236000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06085-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":65,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["6085"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06085-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,1,3]]},"assertion":[{"value":"18 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2025","order":2,"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 no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts interest"}}],"article-number":"257"}}