{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T09:18:35Z","timestamp":1793524715575,"version":"4.5.0"},"reference-count":76,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100019081","name":"Science and Technology Program of Hunan Province","doi-asserted-by":"publisher","award":["2022RC1031"],"award-info":[{"award-number":["2022RC1031"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2225025"],"award-info":[{"award-number":["T2225025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102454"],"award-info":[{"award-number":["62102454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20256"],"award-info":[{"award-number":["U24A20256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008081","name":"Southeast University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002822","name":"Central South University","doi-asserted-by":"publisher","award":["2023CXQD018"],"award-info":[{"award-number":["2023CXQD018"]}],"id":[{"id":"10.13039\/501100002822","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.media.2025.103545","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T04:07:52Z","timestamp":1742184472000},"page":"103545","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":71,"special_numbering":"C","title":["LW-CTrans: A lightweight hybrid network of CNN and Transformer for 3D medical image segmentation"],"prefix":"10.1016","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7341-9871","authenticated-orcid":false,"given":"Hulin","family":"Kuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianzhen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8430-1115","authenticated-orcid":false,"given":"Jialin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiarui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiulou","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4610-2304","authenticated-orcid":false,"given":"Chunfeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5660-6349","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.media.2025.103545_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-30695-9","article-title":"The medical segmentation decathlon","volume":"13","author":"Antonelli","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2025.103545_b2","series-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge","author":"Bakas","year":"2018"},{"key":"10.1016\/j.media.2025.103545_b3","series-title":"Transclaw u-net: Claw u-net with transformers for medical image segmentation","author":"Chang","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., Liu, Z., 2022. Mobile-former: Bridging mobilenet and transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 5270\u20135279.","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"10.1016\/j.media.2025.103545_b5","series-title":"Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b6","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"issue":"6","key":"10.1016\/j.media.2025.103545_b7","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/TMI.2022.3142321","article-title":"A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping","volume":"41","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b8","series-title":"Sam-med2d","author":"Cheng","year":"2023"},{"key":"10.1016\/j.media.2025.103545_b9","series-title":"Inductive bias of deep convolutional networks through pooling geometry","author":"Cohen","year":"2017"},{"key":"10.1016\/j.media.2025.103545_b10","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., Ding, G., 2022. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 11963\u201311975.","DOI":"10.1109\/CVPR52688.2022.01166"},{"issue":"10","key":"10.1016\/j.media.2025.103545_b11","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102904","article-title":"CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation","volume":"89","author":"Gu","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103545_b13","series-title":"ConvFormer: Combining CNN and transformer for medical image segmentation","author":"Gu","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b14","doi-asserted-by":"crossref","unstructured":"Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., Xu, C., 2022. Cmt: Convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 12175\u201312185.","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"10.1016\/j.media.2025.103545_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103274","article-title":"DMSPS: Dynamically mixed soft pseudo-label supervision for scribble-supervised medical image segmentation","volume":"97","author":"Han","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103545_b16","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D., 2022. Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.media.2025.103545_b17","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2023.3264513","article-title":"H2Former: An efficient hierarchical hybrid transformer for medical image segmentation","author":"He","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b18","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M., 2019. Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 558\u2013567.","DOI":"10.1109\/CVPR.2019.00065"},{"key":"10.1016\/j.media.2025.103545_b19","doi-asserted-by":"crossref","unstructured":"Heidari, M., Kazerouni, A., Soltany, M., Azad, R., Aghdam, E.K., Cohen-Adad, J., Merhof, D., 2023. Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 6202\u20136212.","DOI":"10.1109\/WACV56688.2023.00614"},{"issue":"2","key":"10.1016\/j.media.2025.103545_b20","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.media.2025.103545_b21","series-title":"International MICCAI Brainlesion Workshop","first-page":"262","article-title":"Brain tumor segmentation and survival prediction using 3D attention unet","author":"Islam","year":"2019"},{"key":"10.1016\/j.media.2025.103545_b22","series-title":"2020 IEEE 33rd International Symposium on Computer-Based Medical Systems","first-page":"558","article-title":"Doubleu-net: A deep convolutional neural network for medical image segmentation","author":"Jha","year":"2020"},{"key":"10.1016\/j.media.2025.103545_b23","series-title":"APAUNet: Axis projection attention unet for small target in 3D medical segmentation","author":"Jiang","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b24","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al., 2023. Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.media.2025.103545_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103490","article-title":"Large vessel occlusion identification network with vessel guidance and asymmetry learning on CT angiography of acute ischemic stroke patients","author":"Kuang","year":"2025","journal-title":"Med. Image Anal."},{"issue":"10","key":"10.1016\/j.media.2025.103545_b26","doi-asserted-by":"crossref","first-page":"4828","DOI":"10.1109\/JBHI.2023.3304662","article-title":"BEA-net: Body and edge aware network with multi-scale short-term concatenation for medical image segmentation","volume":"27","author":"Kuang","year":"2023","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"10.1016\/j.media.2025.103545_b27","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2024.3362879","article-title":"Hybrid CNN-transformer network with circular feature interaction for acute ischemic stroke lesion segmentation on non-contrast CT scans","author":"Kuang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2025.103545_b28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TMI.2023.3294975","article-title":"One-shot weakly-supervised segmentation in 3D medical images","volume":"43","author":"Lei","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b29","series-title":"Next-ViT: Next generation vision transformer for efficient deployment in realistic industrial scenarios","author":"Li","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b30","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"432","article-title":"Symmetry-enhanced attention network for acute ischemic infarct segmentation with non-contrast CT images","author":"Liang","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b31","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2022.3178991","article-title":"Ds-transunet: Dual swin transformer u-net for medical image segmentation","author":"Lin","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.media.2025.103545_b32","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2023.3266977","article-title":"BATFormer: Towards boundary-aware lightweight transformer for efficient medical image segmentation","author":"Lin","year":"2023","journal-title":"IEEE J. Biomed. Heal. Inform."},{"issue":"8","key":"10.1016\/j.media.2025.103545_b33","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/JBHI.2023.3273771","article-title":"Hybrid contextual semantic network for accurate segmentation and detection of small-size stroke lesions from MRI","volume":"27","author":"Liu","year":"2023","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"10.1016\/j.media.2025.103545_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119166","article-title":"Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation","volume":"214","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.media.2025.103545_b35","article-title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","author":"Liu","year":"2021","journal-title":"Int. Conf. Comput. Vis. ( ICCV)"},{"key":"10.1016\/j.media.2025.103545_b36","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"235","article-title":"Phtrans: Parallelly aggregating global and local representations for medical image segmentation","author":"Liu","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b37","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"615","article-title":"Swin-umamba: Mamba-based unet with imagenet-based pretraining","author":"Liu","year":"2024"},{"key":"10.1016\/j.media.2025.103545_b38","article-title":"Swin-UMamba\u2020: Adapting mamba-based vision foundation models for medical image segmentation","author":"Liu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b39","series-title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"},{"key":"10.1016\/j.media.2025.103545_b40","series-title":"International Conference on Machine Learning","first-page":"23803","article-title":"Cross-entropy loss functions: Theoretical analysis and applications","author":"Mao","year":"2023"},{"key":"10.1016\/j.media.2025.103545_b41","unstructured":"Mehta, S., Rastegari, M., 2022. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer. In: International Conference on Learning Representations."},{"issue":"10","key":"10.1016\/j.media.2025.103545_b42","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b43","series-title":"2016 Fourth International Conference on 3D Vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.media.2025.103545_b44","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"issue":"3","key":"10.1016\/j.media.2025.103545_b45","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TMI.2023.3326188","article-title":"Slim UNETR: Scale hybrid transformers to efficient 3D medical image segmentation under limited computational resources","volume":"43","author":"Pang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b46","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22","first-page":"30","article-title":"One network to segment them all: A general, lightweight system for accurate 3d medical image segmentation","author":"Perslev","year":"2019"},{"key":"10.1016\/j.media.2025.103545_b47","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"247","article-title":"X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies","author":"Qi","year":"2019"},{"key":"10.1016\/j.media.2025.103545_b48","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.media.2025.103545_b49","series-title":"Adaptive split-fusion transformer","author":"Su","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b50","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2025.103545_b51","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"109","article-title":"Transbts: Multimodal brain tumor segmentation using transformer","author":"Wang","year":"2021"},{"issue":"1","key":"10.1016\/j.media.2025.103545_b52","doi-asserted-by":"crossref","first-page":"5915","DOI":"10.1038\/s41467-021-26216-9","article-title":"Annotation-efficient deep learning for automatic medical image segmentation","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2025.103545_b53","series-title":"2022 26th International Conference on Pattern Recognition","first-page":"4623","article-title":"AST-net: Lightweight hybrid transformer for multimodal brain tumor segmentation","author":"Wang","year":"2022"},{"issue":"1","key":"10.1016\/j.media.2025.103545_b54","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s41598-022-27358-6","article-title":"Combining convolutional neural networks and self-attention for fundus diseases identification","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"issue":"7","key":"10.1016\/j.media.2025.103545_b55","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","article-title":"DeepIGeoS: a deep interactive geodesic framework for medical image segmentation","volume":"41","author":"Wang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2025.103545_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102327","article-title":"FAT-Net: Feature adaptive transformers for automated skin lesion segmentation","volume":"76","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103545_b57","first-page":"1","article-title":"D-former: A u-shaped dilated transformer for 3d medical image segmentation","author":"Wu","year":"2022","journal-title":"Neural Comput. Appl."},{"issue":"12","key":"10.1016\/j.media.2025.103545_b58","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1109\/TMI.2023.3318364","article-title":"UPL-SFDA: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation","volume":"42","author":"Wu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103545_b59","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L., 2021. Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 22\u201331.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"10.1016\/j.media.2025.103545_b60","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2025.103545_b61","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"171","article-title":"Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation","author":"Xie","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b62","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109432","article-title":"3D medical image segmentation using parallel transformers","volume":"138","author":"Yan","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.media.2025.103545_b63","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22","first-page":"266","article-title":"CLCI-net: Cross-level fusion and context inference networks for lesion segmentation of chronic stroke","author":"Yang","year":"2019"},{"key":"10.1016\/j.media.2025.103545_b64","series-title":"Dual vision transformer","author":"Yao","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b65","doi-asserted-by":"crossref","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., Yan, S., 2022. Metaformer is actually what you need for vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 10819\u201310829.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"10.1016\/j.media.2025.103545_b66","series-title":"Medical Image Computing and Computer Assisted Intervention","first-page":"14","article-title":"Transfuse: Fusing transformers and cnns for medical image segmentation","author":"Zhang","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b67","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102996","article-title":"On the challenges and perspectives of foundation models for medical image analysis","volume":"91","author":"Zhang","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103545_b68","series-title":"IJCAI","first-page":"4271","article-title":"Light-weight hybrid convolutional network for liver tumor segmentation.","volume":"Vol. 19","author":"Zhang","year":"2019"},{"key":"10.1016\/j.media.2025.103545_b69","series-title":"Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and beyond","author":"Zhang","year":"2022"},{"key":"10.1016\/j.media.2025.103545_b70","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J., 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6848\u20136856.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"10.1016\/j.media.2025.103545_b71","series-title":"2021 IEEE 18th International Symposium on Biomedical Imaging","first-page":"42","article-title":"Lcov-net: A lightweight neural network for covid-19 pneumonia lesion segmentation from 3d ct images","author":"Zhao","year":"2021"},{"key":"10.1016\/j.media.2025.103545_b72","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al., 2021. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 6881\u20136890.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"10.1016\/j.media.2025.103545_b73","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"nnFormer: Volumetric medical image segmentation via a 3D transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.media.2025.103545_b74","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/TCBB.2019.2939522","article-title":"D-UNet: A dimension-fusion u shape network for chronic stroke lesion segmentation","volume":"18","author":"Zhou","year":"2021","journal-title":"IEEE\/ ACM Trans. Comput. Biology Bioinform."},{"key":"10.1016\/j.media.2025.103545_b75","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114566","article-title":"ERV-Net: An efficient 3D residual neural network for brain tumor segmentation","volume":"170","author":"Zhou","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.media.2025.103545_b76","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525000921?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525000921?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T20:00:36Z","timestamp":1762372836000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841525000921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":76,"alternative-id":["S1361841525000921"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103545","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LW-CTrans: A lightweight hybrid network of CNN and Transformer for 3D medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103545","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103545"}}