{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:05:59Z","timestamp":1780092359843,"version":"3.54.0"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.compmedimag.2026.102727","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T16:29:21Z","timestamp":1771604961000},"page":"102727","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["LCBTS-Net: A lightweight cascaded 3D brain tumor segmentation network in magnetic resonance imaging"],"prefix":"10.1016","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8252-1728","authenticated-orcid":false,"given":"Ayse Bastug","family":"Koc","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0770-599X","authenticated-orcid":false,"given":"Devrim","family":"Akgun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2026.102727_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2024.101892","article-title":"3DUV-NetR+: A 3D hybrid semantic architecture using transformers for brain tumor segmentation with MultiModal MR images","volume":"21","author":"Aboussaleh","year":"2024","journal-title":"Results Eng."},{"key":"10.1016\/j.compmedimag.2026.102727_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102313","article-title":"A review on brain tumor segmentation based on deep learning methods with federated learning techniques","volume":"110","author":"Ahamed","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"issue":"13","key":"10.1016\/j.compmedimag.2026.102727_b3","doi-asserted-by":"crossref","first-page":"7529","DOI":"10.1007\/s00521-024-09475-7","article-title":"Yaru3DFPN: a lightweight modified 3D unet with feature pyramid network and combine thresholding for brain tumor segmentation","volume":"36","author":"Akbar","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.compmedimag.2026.102727_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102332","article-title":"Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation","volume":"112","author":"Al Khalil","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.compmedimag.2026.102727_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109353","article-title":"LATUP-Net: A lightweight 3D attention U-net with parallel convolutions for brain tumor segmentation","volume":"184","author":"Alwadee","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2026.102727_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107490","article-title":"BTS U-Net: A data-driven approach to brain tumor segmentation through deep learning","volume":"104","author":"Aumente-Maestro","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compmedimag.2026.102727_b7","series-title":"The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification","author":"Baid","year":"2021"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102727_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.compmedimag.2026.102727_b9","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.compmedimag.2026.102727_b10","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"424","article-title":"3D U-net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.compmedimag.2026.102727_b11","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II 6","first-page":"410","article-title":"DR-unet104 for multimodal MRI brain tumor segmentation","author":"Colman","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102727_b12","series-title":"The 2024 brain tumor segmentation (BraTS) challenge: glioma segmentation on post-treatment MRI","author":"de Verdier","year":"2024"},{"issue":"22","key":"10.1016\/j.compmedimag.2026.102727_b13","doi-asserted-by":"crossref","first-page":"34809","DOI":"10.1007\/s11042-023-14857-5","article-title":"Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging-a review","volume":"82","author":"Domadia","year":"2023","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.compmedimag.2026.102727_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmp.2024.103304","article-title":"Segmenting brain glioblastoma using dense-attentive 3D DAF2","volume":"119","author":"Domadia","year":"2024","journal-title":"Phys. Medica"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102727_b15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41698-024-00789-2","article-title":"A review of deep learning for brain tumor analysis in MRI","volume":"9","author":"Dorfner","year":"2025","journal-title":"NPJ Precis. Oncol."},{"key":"10.1016\/j.compmedimag.2026.102727_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111348","article-title":"DAUnet: A U-shaped network combining deep supervision and attention for brain tumor segmentation","volume":"285","author":"Feng","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.compmedimag.2026.102727_b17","series-title":"International MICCAI Brainlesion Workshop","first-page":"15","article-title":"Optimized U-net for brain tumor segmentation","author":"Futrega","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102727_b18","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6","first-page":"481","article-title":"Automated brain tumour segmentation using cascaded 3d densely-connected u-net","author":"Ghaffari","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102727_b19","series-title":"International MICCAI Brainlesion Workshop","first-page":"272","article-title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102727_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.compmedimag.2026.102727_b21","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II 6","first-page":"118","article-title":"nnU-net for brain tumor segmentation","author":"Isensee","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102727_b22","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5","first-page":"231","article-title":"Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task","author":"Jiang","year":"2020"},{"key":"10.1016\/j.compmedimag.2026.102727_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106911","article-title":"TDPC-net: Multi-scale lightweight and efficient 3D segmentation network with a 3D attention mechanism for brain tumor segmentation","volume":"99","author":"Li","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compmedimag.2026.102727_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103784","article-title":"3D PSwinBTS: an efficient transformer-based unet using 3D parallel shifted windows for brain tumor segmentation","volume":"131","author":"Liang","year":"2022","journal-title":"Digit. Signal Process."},{"issue":"8","key":"10.1016\/j.compmedimag.2026.102727_b25","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TMI.2023.3250474","article-title":"CKD-TransBTS: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation","volume":"42","author":"Lin","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102727_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102776","article-title":"A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network","volume":"148","author":"Liu","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102727_b27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/jimaging7020019","article-title":"Deep learning for brain tumor segmentation: a survey of state-of-the-art","volume":"7","author":"Magadza","year":"2021","journal-title":"J. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102727_b28","doi-asserted-by":"crossref","first-page":"126386","DOI":"10.1109\/ACCESS.2023.3329517","article-title":"Efficient nnu-net for brain tumor segmentation","volume":"11","author":"Magadza","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2026.102727_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103077","article-title":"Attention res-UNet with guided decoder for semantic segmentation of brain tumors","volume":"71","author":"Maji","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"10","key":"10.1016\/j.compmedimag.2026.102727_b30","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.compmedimag.2026.102727_b31","doi-asserted-by":"crossref","first-page":"69884","DOI":"10.1109\/ACCESS.2023.3294179","article-title":"ResUNet+: A new convolutional and attention block-based approach for brain tumor segmentation","volume":"11","author":"Metlek","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.compmedimag.2026.102727_b32","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.compmedimag.2026.102727_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109662","article-title":"VcaNet: Vision transformer with fusion channel and spatial attention module for 3D brain tumor segmentation","volume":"186","author":"Pan","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.compmedimag.2026.102727_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104336","article-title":"The multimodal MRI brain tumor segmentation based on AD-net","volume":"80","author":"Peng","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compmedimag.2026.102727_b35","series-title":"2022 IEEE 19th International Symposium on Biomedical Imaging","first-page":"1","article-title":"Segtransvae: Hybrid cnn-transformer with regularization for medical image segmentation","author":"Pham","year":"2022"},{"key":"10.1016\/j.compmedimag.2026.102727_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103861","article-title":"dResU-Net: 3D deep residual U-net based brain tumor segmentation from multimodal MRI","volume":"79","author":"Raza","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compmedimag.2026.102727_b37","doi-asserted-by":"crossref","DOI":"10.1109\/LSENS.2024.3370974","article-title":"Multiclass tumor segmentation from brain MRIs using GARU-net: Gelu activated attention aware res-3DUNET for adaptive feature pooling","author":"Raza","year":"2024","journal-title":"IEEE Sensors Lett."},{"key":"10.1016\/j.compmedimag.2026.102727_b38","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"2","key":"10.1016\/j.compmedimag.2026.102727_b39","doi-asserted-by":"crossref","first-page":"185","DOI":"10.3390\/bioengineering12020185","article-title":"Intracranial aneurysm segmentation with a dual-path fusion network","volume":"12","author":"Wang","year":"2025","journal-title":"Bioengineering"},{"issue":"7","key":"10.1016\/j.compmedimag.2026.102727_b40","doi-asserted-by":"crossref","first-page":"3420","DOI":"10.3390\/s23073420","article-title":"High-resolution swin transformer for automatic medical image segmentation","volume":"23","author":"Wei","year":"2023","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.compmedimag.2026.102727_b41","doi-asserted-by":"crossref","first-page":"4448","DOI":"10.1038\/s41598-025-87127-z","article-title":"A deep ensemble learning framework for glioma segmentation and grading prediction","volume":"15","author":"Wen","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2026.102727_b42","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer","first-page":"109","article-title":"Transbts: Multimodal brain tumor segmentation using transformer","author":"Wenxuan","year":"2021"},{"key":"10.1016\/j.compmedimag.2026.102727_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108005","article-title":"ETUNet: Exploring efficient transformer enhanced unet for 3D brain tumor segmentation","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"12","key":"10.1016\/j.compmedimag.2026.102727_b44","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1109\/TMI.2022.3197180","article-title":"Prior attention network for multi-lesion segmentation in medical images","volume":"41","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2026.102727_b45","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.inffus.2022.10.022","article-title":"Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI","volume":"91","author":"Zhu","year":"2023","journal-title":"Inf. Fusion"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000303?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611126000303?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:00:08Z","timestamp":1773219608000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611126000303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":45,"alternative-id":["S0895611126000303"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102727","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"LCBTS-Net: A lightweight cascaded 3D brain tumor segmentation network in magnetic resonance imaging","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2026.102727","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102727"}}