{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T03:11:11Z","timestamp":1782702671990,"version":"3.54.5"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2221-E-002-080-MY3"],"award-info":[{"award-number":["MOST 108-2221-E-002-080-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 107-2320-B-002-043-MY3"],"award-info":[{"award-number":["MOST 107-2320-B-002-043-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Experimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the development of pathological extent. An accurate and automatic skull stripping tool for rat brain image volumes with magnetic resonance imaging (MRI) are crucial in experimental stroke analysis. Due to the deficiency of reliable rat brain segmentation methods and motivated by the demand for preclinical studies, this paper develops a new skull stripping algorithm to extract the rat brain region in MR images after stroke, which is named Rat U-Net (RU-Net).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Based on a U-shape like deep learning architecture, the proposed framework integrates batch normalization with the residual network to achieve efficient end-to-end segmentation. A pooling index transmission mechanism between the encoder and decoder is exploited to reinforce the spatial correlation. Two different modalities of diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) corresponding to two in-house datasets with each consisting of 55 subjects were employed to evaluate the performance of the proposed RU-Net.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Extensive experiments indicated great segmentation accuracy across diversified rat brain MR images. It was suggested that our rat skull stripping network outperformed several state-of-the-art methods and achieved the highest average Dice scores of 98.04% (p\u2009&lt;\u20090.001) and 97.67% (p\u2009&lt;\u20090.001) in the DWI and T2WI image datasets, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The proposed RU-Net is believed to be potential for advancing preclinical stroke investigation and providing an efficient tool for pathological rat brain image extraction, where accurate segmentation of the rat brain region is fundamental.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-00994-8","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T16:04:09Z","timestamp":1679933049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net"],"prefix":"10.1186","volume":"23","author":[{"given":"Herng-Hua","family":"Chang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin-Joe","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Chang","family":"Chiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Tsang","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"994_CR1","doi-asserted-by":"crossref","unstructured":"et al: Heart Disease and Stroke Statistics\u20142020 Update: A Report From the American Heart Association. Circulation 2020, 141(9):e139-e596.","DOI":"10.1161\/CIR.0000000000000746"},{"issue":"6","key":"994_CR2","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/s11886-018-0989-4","volume":"20","author":"R Khatri","year":"2018","unstructured":"Khatri R, Vellipuram AR, Maud A, Cruz-Flores S, Rodriguez GJ. Current Endovascular Approach to the management of Acute ischemic stroke. Curr Cardiol Rep. 2018;20(6):46.","journal-title":"Curr Cardiol Rep"},{"key":"994_CR3","first-page":"3445","volume":"9","author":"F Fluri","year":"2015","unstructured":"Fluri F, Schuhmann MK, Kleinschnitz C. Animal models of ischemic stroke and their application in clinical research. Drug Des Devel Ther. 2015;9:3445\u201354.","journal-title":"Drug Des Devel Ther"},{"issue":"4","key":"994_CR4","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s12975-017-0590-y","volume":"9","author":"IL Gubskiy","year":"2018","unstructured":"Gubskiy IL, Namestnikova DD, Cherkashova EA, Chekhonin VP, Baklaushev VP, Gubsky LV, Yarygin KN. MRI guiding of the Middle cerebral artery occlusion in rats aimed to Improve Stroke modeling. Translational Stroke Research. 2018;9(4):417\u201325.","journal-title":"Translational Stroke Research"},{"issue":"1","key":"994_CR5","doi-asserted-by":"publisher","first-page":"4989","DOI":"10.1038\/s41598-020-61656-1","volume":"10","author":"M Kang","year":"2020","unstructured":"Kang M, Jin S, Lee D, Cho H. MRI visualization of whole brain macro- and microvascular remodeling in a rat model of ischemic stroke: a pilot study. Sci Rep. 2020;10(1):4989.","journal-title":"Sci Rep"},{"issue":"5","key":"994_CR6","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1002\/jmri.25667","volume":"46","author":"Y Li","year":"2017","unstructured":"Li Y, Zhu X, Ju S, Yan J, Wang D, Zhu Y, Zang F. Detection of volume alterations in hippocampal subfields of rats under chronic unpredictable mild stress using 7T MRI: a follow-up study. J Magn Reson Imaging. 2017;46(5):1456\u201363.","journal-title":"J Magn Reson Imaging"},{"key":"994_CR7","first-page":"3","volume":"11","author":"IA Mulder","year":"2017","unstructured":"Mulder IA, Khmelinskii A, Dzyubachyk O, de Jong S, Rieff N, Wermer MJH, Hoehn M, van den Lelieveldt BPF. Maagdenberg AMJM: automated ischemic lesion segmentation in MRI Mouse Brain Data after transient middle cerebral artery occlusion. Front Neuroinform. 2017;11:3\u20133.","journal-title":"Front Neuroinform"},{"key":"994_CR8","doi-asserted-by":"crossref","unstructured":"Yeh S-J, Tang S-C, Tsai L-K, Jeng J-S, Chen C-L, Hsieh S-T. Neuroanatomy- and Pathology-Based Functional Examinations of Experimental Stroke in Rats: Development and Validation of a New Behavioral Scoring System.Frontiers in Behavioral Neuroscience2018, 12(316).","DOI":"10.3389\/fnbeh.2018.00316"},{"key":"994_CR9","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.neuroscience.2018.07.045","volume":"388","author":"A Aliena-Valero","year":"2018","unstructured":"Aliena-Valero A, L\u00f3pez-Morales MA, Burguete MC, Castell\u00f3-Ruiz M, Jover-Mengual T, Herv\u00e1s D, Torregrosa G, Leira EC, Chamorro \u00c1, Salom JB. Emergent Uric Acid Treatment is synergistic with mechanical recanalization in improving stroke outcomes in male and female rats. Neuroscience. 2018;388:263\u201373.","journal-title":"Neuroscience"},{"issue":"3","key":"994_CR10","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s12021-020-09453-z","volume":"18","author":"Y Liu","year":"2020","unstructured":"Liu Y, Unsal HS, Tao Y, Zhang N. Automatic brain extraction for Rodent MRI images. Neuroinformatics. 2020;18(3):395\u2013406.","journal-title":"Neuroinformatics"},{"issue":"1","key":"994_CR11","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12880-022-00867-6","volume":"22","author":"S-M Huang","year":"2022","unstructured":"Huang S-M, Wu C-Y, Lin Y-H, Hsieh H-H, Yang H-C, Chiu S-C, Peng S-L. Differences in brain activity between normal and diabetic rats under isoflurane anesthesia: a resting-state functional MRI study. BMC Med Imaging. 2022;22(1):136.","journal-title":"BMC Med Imaging"},{"issue":"10","key":"994_CR12","doi-asserted-by":"publisher","first-page":"5756","DOI":"10.1002\/mp.15210","volume":"48","author":"A Nemani","year":"2021","unstructured":"Nemani A, Lowe MJ. Seed-based test\u2013retest reliability of resting state functional magnetic resonance imaging at 3T and 7T. Med Phys. 2021;48(10):5756\u201364.","journal-title":"Med Phys"},{"issue":"1","key":"994_CR13","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.jneumeth.2013.04.011","volume":"218","author":"J Li","year":"2013","unstructured":"Li J, Liu X, Zhuo J, Gullapalli RP, Zara JM. An automatic rat brain extraction method based on a deformable surface model. J Neurosci Methods. 2013;218(1):72\u201382.","journal-title":"J Neurosci Methods"},{"key":"994_CR14","doi-asserted-by":"crossref","unstructured":"Oguz I, Lee J, Budin F, Rumple A, McMurray M, Ehlers C, Crews F, Johns J, Styner M. Automatic skull-stripping of rat MRI\/DTI scans and atlas building. Volume 7962. SPIE; 2011.","DOI":"10.1117\/12.878405"},{"issue":"10","key":"994_CR15","doi-asserted-by":"publisher","first-page":"e109113","DOI":"10.1371\/journal.pone.0109113","volume":"9","author":"S Lancelot","year":"2014","unstructured":"Lancelot S, Roche R, Slimen A, Bouillot C, Levigoureux E, Langlois J-B, Zimmer L, Costes N. A Multi-Atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity. PLoS ONE. 2014;9(10):e109113.","journal-title":"PLoS ONE"},{"key":"994_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.jneumeth.2015.09.031","volume":"257","author":"A Delora","year":"2016","unstructured":"Delora A, Gonzales A, Medina CS, Mitchell A, Mohed AF, Jacobs RE, Bearer EL. A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head. J Neurosci Methods. 2016;257:185\u201393.","journal-title":"J Neurosci Methods"},{"key":"994_CR17","doi-asserted-by":"crossref","unstructured":"Huang W, Zhang J, Lin Z, Huang S, Duan Y, Lu Z. Template based rodent brain extraction and atlas mapping. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 16\u201320 Aug. 2016 2016; 2016: 4063\u20134066.","DOI":"10.1109\/EMBC.2016.7591619"},{"issue":"Pt 3","key":"994_CR18","first-page":"611","volume":"14","author":"S Zhang","year":"2011","unstructured":"Zhang S, Huang J, Uzunbas M, Shen T, Delis F, Huang X, Volkow N, Thanos P, Metaxas DN. 3D segmentation of rodent brain structures using hierarchical shape priors and deformable models. Med Image Comput Comput Assist Interv. 2011;14(Pt 3):611\u20138.","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"994_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.jneumeth.2013.09.021","volume":"221","author":"I Oguz","year":"2014","unstructured":"Oguz I, Zhang H, Rumple A, Sonka M. RATS: Rapid Automatic tissue segmentation in rodent brain MRI. J Neurosci Methods. 2014;221:175\u201382.","journal-title":"J Neurosci Methods"},{"issue":"17","key":"994_CR20","doi-asserted-by":"publisher","first-page":"4952","DOI":"10.1002\/hbm.24750","volume":"40","author":"F Isensee","year":"2019","unstructured":"Isensee F, Schell M, Pflueger I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer H-P, Heiland S, Wick W, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp. 2019;40(17):4952\u201364.","journal-title":"Hum Brain Mapp"},{"issue":"4","key":"994_CR21","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1007\/s10278-021-00486-7","volume":"34","author":"MJ Ali","year":"2021","unstructured":"Ali MJ, Raza B, Shahid AR. Multi-level kronecker convolutional neural network (ML-KCNN) for glioma segmentation from multi-modal MRI Volumetric Data. J Digit Imaging. 2021;34(4):905\u201321.","journal-title":"J Digit Imaging"},{"issue":"8","key":"994_CR22","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1002\/mp.14201","volume":"47","author":"Z Yang","year":"2020","unstructured":"Yang Z, Liu H, Liu Y, Stojadinovic S, Timmerman R, Nedzi L, Dan T, Wardak Z, Lu W, Gu X. A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery. Med Phys. 2020;47(8):3263\u201376.","journal-title":"Med Phys"},{"key":"994_CR23","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.artmed.2018.08.008","volume":"95","author":"J Bernal","year":"2019","unstructured":"Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Mart\u00ed R, Llad\u00f3 X. Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med. 2019;95:64\u201381.","journal-title":"Artif Intell Med"},{"issue":"3","key":"994_CR24","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.neuroimage.2008.12.021","volume":"45","author":"M Murugavel","year":"2009","unstructured":"Murugavel M, Sullivan JM. Automatic cropping of MRI rat brain volumes using pulse coupled neural networks. NeuroImage. 2009;45(3):845\u201354.","journal-title":"NeuroImage"},{"issue":"9","key":"994_CR25","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1109\/TIP.2011.2126587","volume":"20","author":"N Chou","year":"2011","unstructured":"Chou N, Wu J, Bingren JB, Qiu A, Chuang K. Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN). IEEE Trans Image Process. 2011;20(9):2554\u201364.","journal-title":"IEEE Trans Image Process"},{"key":"994_CR26","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume\u00a01 Lake Tahoe, Nevada: Curran Associates Inc.; 2012: 1097\u20131105."},{"key":"994_CR27","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T.Fully convolutional networks for semantic segmentation; 2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"994_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: 2015; Cham:Springer International Publishing; 2015:pp.\u00a0234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"994_CR29","doi-asserted-by":"publisher","first-page":"101764","DOI":"10.1016\/j.compmedimag.2020.101764","volume":"84","author":"X Ding","year":"2020","unstructured":"Ding X, Peng Y, Shen C, Zeng T. CAB U-Net: an end-to-end category attention boosting algorithm for segmentation. Comput Med Imaging Graph. 2020;84:101764.","journal-title":"Comput Med Imaging Graph"},{"key":"994_CR30","doi-asserted-by":"publisher","first-page":"101920","DOI":"10.1016\/j.compmedimag.2021.101920","volume":"90","author":"S Mizusawa","year":"2021","unstructured":"Mizusawa S, Sei Y, Orihara R, Ohsuga A. Computed tomography image reconstruction using stacked U-Net. Comput Med Imaging Graph. 2021;90:101920.","journal-title":"Comput Med Imaging Graph"},{"key":"994_CR31","doi-asserted-by":"publisher","first-page":"101908","DOI":"10.1016\/j.compmedimag.2021.101908","volume":"90","author":"V Abramova","year":"2021","unstructured":"Abramova V, Cl\u00e8rigues A, Quiles A, Figueredo DG, Silva Y, Pedraza S, Oliver A, Llad\u00f3 X. Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks. Comput Med Imaging Graph. 2021;90:101908.","journal-title":"Comput Med Imaging Graph"},{"key":"994_CR32","doi-asserted-by":"crossref","unstructured":"Hsu L-M, Wang S, Ranadive P, Ban W, Chao T-HH, Song S, Cerri DH, Walton LR, Broadwater MA, Lee S-H et al. Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net.Frontiers in Neuroscience2020, 14(935).","DOI":"10.3389\/fnins.2020.568614"},{"key":"994_CR33","doi-asserted-by":"publisher","first-page":"117734","DOI":"10.1016\/j.neuroimage.2021.117734","volume":"229","author":"R De Feo","year":"2021","unstructured":"De Feo R, Shatillo A, Sierra A, Valverde JM, Gr\u00f6hn O, Giove F, Tohka J. Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases. NeuroImage. 2021;229:117734.","journal-title":"NeuroImage"},{"key":"994_CR34","doi-asserted-by":"crossref","unstructured":"Valverde JM, Shatillo A, De Feo R, Tohka J. Automatic cerebral hemisphere segmentation in Rat MRI with ischemic lesions via attention-based Convolutional neural networks. Neuroinformatics; 2022.","DOI":"10.1007\/s12021-022-09607-1"},{"key":"994_CR35","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition.Proc Int Conf Learn2015.","DOI":"10.1109\/CVPR.2016.90"},{"key":"994_CR36","unstructured":"Ioffe S, Szegedy C. Batch normalization. Accelerating Deep Network Training by Reducing Internal Covariate Shift; 2015."},{"issue":"10","key":"994_CR37","doi-asserted-by":"publisher","first-page":"2932","DOI":"10.1161\/STROKEAHA.110.612788","volume":"42","author":"L-K Tsai","year":"2011","unstructured":"Tsai L-K, Wang Z, Munasinghe J, Leng Y, Leeds P, Chuang D-M. Mesenchymal stem cells primed with Valproate and Lithium robustly migrate to infarcted regions and facilitate recovery in a stroke model. Stroke. 2011;42(10):2932\u20139.","journal-title":"Stroke"},{"key":"994_CR38","unstructured":"Kreyszig E. Advanced Engineering Mathematics. 10th ed. Wiley; 2011."},{"issue":"10","key":"994_CR39","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1109\/TMI.2012.2206398","volume":"31","author":"W Gomez","year":"2012","unstructured":"Gomez W, Pereira WCA, Infantosi AFC. Analysis of Co-Occurrence Texture Statistics as a function of Gray-Level quantization for classifying breast Ultrasound. IEEE Trans Med Imaging. 2012;31(10):1889\u201399.","journal-title":"IEEE Trans Med Imaging"},{"key":"994_CR40","doi-asserted-by":"crossref","unstructured":"Noh H, Hong S, Han B. Learning Deconvolution Network for Semantic Segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV): 7\u201313 Dec. 2015 2015; 2015: 1520\u20131528.","DOI":"10.1109\/ICCV.2015.178"},{"issue":"3","key":"994_CR41","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297\u2013302.","journal-title":"Ecology"},{"key":"994_CR42","unstructured":"Kingma D, Ba J. Adam: A Method for Stochastic Optimization; 2014."},{"issue":"1","key":"994_CR43","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neuroimage.2009.03.068","volume":"47","author":"H-H Chang","year":"2009","unstructured":"Chang H-H, Zhuang AH, Valentino DJ, Chu W-C. Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage. 2009;47(1):122\u201335.","journal-title":"NeuroImage"},{"issue":"3","key":"994_CR44","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/0020-0190(91)90233-8","volume":"38","author":"G Rote","year":"1991","unstructured":"Rote G. Computing the minimum Hausdorff distance between two point sets on a line under translation. Inform Process Lett. 1991;38(3):123\u20137.","journal-title":"Inform Process Lett"},{"key":"994_CR45","unstructured":"Chollet F. Keras Documentation: Francisco: Keras.Io.; 2015."},{"key":"994_CR46","unstructured":"Nvidia C. Tesla K40 GPU Accelerator Overview. In.; 2014."},{"key":"994_CR47","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1006\/nimg.2000.0730","volume":"13","author":"DW Shattuck","year":"2001","unstructured":"Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic resonance image tissue classification using a partial volume model. NeuroImage. 2001;13:856\u201376.","journal-title":"NeuroImage"},{"key":"994_CR48","unstructured":"Wood T, Lythgoe D, Williams S. rBET: Making BET work for Rodent Brains. In: 2013; 2013."},{"key":"994_CR49","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61\u201378.","journal-title":"Med Image Anal"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00994-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-00994-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00994-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T16:05:35Z","timestamp":1679933135000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-00994-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,27]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["994"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-00994-8","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,27]]},"assertion":[{"value":"1 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The study was conducted according to the guidelines of the Basel Declaration, and approved by the Institutional Animal Care and Use Committee (IACUC) of National Taiwan University College of Medicine (protocol code: 20180432 and date of approval: Jan 14 2019). All methods for the reporting of animal experiments were reported in accordance with the ARRIVE guidelines.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"44"}}