{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:11:07Z","timestamp":1778512267638,"version":"3.51.4"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["KJQN202501155"],"award-info":[{"award-number":["KJQN202501155"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004867","name":"Chongqing University of Technology","doi-asserted-by":"publisher","award":["0103240039"],"award-info":[{"award-number":["0103240039"]}],"id":[{"id":"10.13039\/501100004867","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.mri.2025.110505","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T06:27:42Z","timestamp":1755239262000},"page":"110505","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["RCLM-net: A rich-club properties based lightweight deep neural network for multimodal sequence spine MR images segmentation"],"prefix":"10.1016","volume":"123","author":[{"given":"Siyuan","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengchao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.mri.2025.110505_bb0005","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00247-021-05042-7","article-title":"Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging","volume":"52","author":"Serai","year":"2022","journal-title":"Pediatr Radiol"},{"issue":"4","key":"10.1016\/j.mri.2025.110505_bb0010","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11604-017-0621-3","article-title":"Clinical utility of optimized three-dimensional T1-, T2-, and T2*-weighted sequences in spinal magnetic resonance imaging","volume":"35","author":"Tanitame","year":"2017","journal-title":"Jpn J Radiol"},{"issue":"10","key":"10.1016\/j.mri.2025.110505_bb0015","doi-asserted-by":"crossref","first-page":"6730","DOI":"10.1007\/s00330-022-08965-3","article-title":"Differentiation of bone metastases from benign red marrow depositions of the spine: the role of fat-suppressed T2-weighted imaging compared to fat fraction map","volume":"32","author":"Park","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.mri.2025.110505_bb0020","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.neucom.2022.07.070","article-title":"A review on the use of deep learning for medical images segmentation","volume":"506","author":"Aljabri","year":"2022","journal-title":"Neurocomputing"},{"issue":"6","key":"10.1016\/j.mri.2025.110505_bb0025","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.21037\/qims-21-939","article-title":"Current development and prospects of deep learning in spine image analysis: a literature review","volume":"12","author":"Qu","year":"2022","journal-title":"Quant Imaging Med Surg"},{"key":"10.1016\/j.mri.2025.110505_bb0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105105","article-title":"Fully automated segmentation of lumbar bone marrow in sagittal, high-resolution T1-weighted magnetic resonance images using 2D U-NET","volume":"140","author":"Hwang","year":"2022","journal-title":"Comput Biol Med"},{"issue":"4","key":"10.1016\/j.mri.2025.110505_bb0035","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s11517-023-02963-3","article-title":"SALW-net: a lightweight convolutional neural network based on self-adjusting loss function for spine MR image segmentation","volume":"62","author":"He","year":"2024","journal-title":"Med Biol Eng Comput"},{"issue":"5","key":"10.1016\/j.mri.2025.110505_bb0040","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3390\/bioengineering11050427","article-title":"A novel Mis-Seg-focus loss function based on a two-stage nnU-net framework for accurate brain tissue segmentation","volume":"11","author":"He","year":"2024","journal-title":"Bioengineering"},{"key":"10.1016\/j.mri.2025.110505_bb0045","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":"Inform Fusion"},{"issue":"3","key":"10.1016\/j.mri.2025.110505_bb0050","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TMI.2020.3036584","article-title":"Disentangle, align and fuse for multimodal and semi-supervised image segmentation","volume":"40","author":"Chartsias","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.mri.2025.110505_bb0055","article-title":"A review of AI edge devices and lightweight CNN deployment","author":"Sun","year":"2024","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.mri.2025.110505_bb0060","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1007\/s11831-023-10032-z","article-title":"Review of lightweight deep convolutional neural networks","volume":"31","author":"Chen","year":"2024","journal-title":"Arch Comput Methods Eng"},{"issue":"3","key":"10.1016\/j.mri.2025.110505_bb0065","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.bbrc.2020.04.134","article-title":"Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet","volume":"530","author":"Li","year":"2020","journal-title":"Biochem Biophys Res Commun"},{"issue":"6","key":"10.1016\/j.mri.2025.110505_bb0070","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1002\/jmri.28735","article-title":"LSW-net: lightweight deep neural network based on small-world properties for spine MR image segmentation","volume":"58","author":"He","year":"2023","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.mri.2025.110505_bb0075","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.csbj.2020.06.039","article-title":"Rich-club in the brain's macrostructure: insights from graph theoretical analysis","volume":"18","author":"D.-J. Kim","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"issue":"2","key":"10.1016\/j.mri.2025.110505_bb0080","doi-asserted-by":"crossref","first-page":"121","DOI":"10.31887\/DCNS.2018.20.2\/agriffa","article-title":"Rich-club neurocircuitry: function, evolution, and vulnerability","volume":"20","author":"Griffa","year":"2018","journal-title":"Dialogues Clin Neurosci"},{"key":"10.1016\/j.mri.2025.110505_bb0085","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2403","article-title":"Deep layer aggregation","author":"Yu","year":"2018"},{"issue":"12","key":"10.1016\/j.mri.2025.110505_bb0090","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1016\/j.acra.2019.07.006","article-title":"Automated segmentation of tissues using CT and MRI: a systematic review","volume":"26","author":"Lenchik","year":"2019","journal-title":"Acad Radiol"},{"key":"10.1016\/j.mri.2025.110505_bb0095","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/RBME.2022.3185292","article-title":"Image segmentation for MR brain tumor detection using machine learning: a review","volume":"16","author":"Soomro","year":"2022","journal-title":"IEEE Rev Biomed Eng"},{"key":"10.1016\/j.mri.2025.110505_bb0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2021.103248","article-title":"High-level prior-based loss functions for medical image segmentation: a survey","volume":"210","author":"El Jurdi","year":"2021","journal-title":"Comput Vis Image Underst"},{"key":"10.1016\/j.mri.2025.110505_bb0105","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: the success of u-net","author":"Azad","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10.1016\/j.mri.2025.110505_bb0110","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s11517-023-02965-1","article-title":"MresU-net: multi-scale residual U-net-based brain tumor segmentation from multimodal MRI","volume":"62","author":"Li","year":"2024","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.mri.2025.110505_bb0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102528","article-title":"Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge","volume":"81","author":"Zhuang","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.mri.2025.110505_bb0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106074","article-title":"Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images","volume":"205","author":"Das","year":"2021","journal-title":"Comput Methods Prog Biomed"},{"key":"10.1016\/j.mri.2025.110505_bb0125","doi-asserted-by":"crossref","first-page":"11589","DOI":"10.1007\/s00521-021-05856-4","article-title":"Automatic lumbar spinal MRI image segmentation with a multi-scale attention network","volume":"33","author":"Li","year":"2021","journal-title":"Neural Comput & Applic"},{"issue":"12","key":"10.1016\/j.mri.2025.110505_bb0130","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.1109\/TNNLS.2020.3027308","article-title":"Automatic searching and pruning of deep neural networks for medical imaging diagnostic","volume":"32","author":"Fernandes","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"10.1016\/j.mri.2025.110505_bb0135","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: a survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int J Comput Vis"},{"key":"10.1016\/j.mri.2025.110505_bb0140","series-title":"A survey of quantization methods for efficient neural network inference, Low-power computer vision","first-page":"291","author":"Gholami","year":"2022"},{"key":"10.1016\/j.mri.2025.110505_bb0145","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"issue":"4","key":"10.1016\/j.mri.2025.110505_bb0150","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11263-019-01283-0","article-title":"A simple and light-weight attention module for convolutional neural networks","volume":"128","author":"Park","year":"2020","journal-title":"Intern J Comp Vision"},{"issue":"10","key":"10.1016\/j.mri.2025.110505_bb0155","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1093\/cercor\/bhu252","article-title":"Functional clusters, hubs, and communities in the cortical microconnectome","volume":"25","author":"Shimono","year":"2015","journal-title":"Cereb Cortex"},{"issue":"2","key":"10.1016\/j.mri.2025.110505_bb0160","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/nphys209","article-title":"Detecting rich-club ordering in complex networks","volume":"2","author":"Colizza","year":"2006","journal-title":"Nat Phys"},{"key":"10.1016\/j.mri.2025.110505_bb0165","series-title":"U-net: Convolutional networks for biomedical image segmentation. 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","author":"Ronneberger","year":"2015"},{"issue":"6","key":"10.1016\/j.mri.2025.110505_bb0170","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"10.1016\/j.mri.2025.110505_bb0175","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J Med Imag"},{"key":"10.1016\/j.mri.2025.110505_bb0180","series-title":"2021 IEEE international conference on consumer electronics and computer engineering (ICCECE)","first-page":"353","article-title":"RAU-Net: U-Net model based on residual and attention for kidney and kidney tumor segmentation","author":"Guo","year":"2021"},{"key":"10.1016\/j.mri.2025.110505_bb0185","series-title":"International workshop on machine learning in medical imaging","first-page":"379","article-title":"Tversky loss function for image segmentation using 3D fully convolutional deep networks","author":"Salehi","year":"2017"},{"key":"10.1016\/j.mri.2025.110505_bb0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102035","article-title":"Loss odyssey in medical image segmentation","volume":"71","author":"Ma","year":"2021","journal-title":"Med Image Anal"},{"key":"10.1016\/j.mri.2025.110505_bb0195","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","article-title":"Combo loss: handling input and output imbalance in multi-organ segmentation","volume":"75","author":"Taghanaki","year":"2019","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.mri.2025.110505_bb0200","doi-asserted-by":"crossref","first-page":"164546","DOI":"10.1109\/ACCESS.2020.3021739","article-title":"Semantic segmentation of litchi branches using deeplabv3+ model","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.mri.2025.110505_bb0205","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.mri.2025.110505_bb0210","article-title":"Enet: a deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016","journal-title":"arXiv"},{"issue":"1","key":"10.1016\/j.mri.2025.110505_bb0215","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/TMI.2020.3025087","article-title":"Spineparsenet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation","volume":"40","author":"Pang","year":"2020","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0730725X25001894?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0730725X25001894?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T14:29:55Z","timestamp":1778509795000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0730725X25001894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":43,"alternative-id":["S0730725X25001894"],"URL":"https:\/\/doi.org\/10.1016\/j.mri.2025.110505","relation":{},"ISSN":["0730-725X"],"issn-type":[{"value":"0730-725X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RCLM-net: A rich-club properties based lightweight deep neural network for multimodal sequence spine MR images segmentation","name":"articletitle","label":"Article Title"},{"value":"Magnetic Resonance Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mri.2025.110505","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110505"}}