{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:08:01Z","timestamp":1770901681966,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"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":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11517-022-02752-4","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T04:28:45Z","timestamp":1673324925000},"page":"847-865","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An end-end deep learning framework for lesion segmentation on multi-contrast MR images\u2014an exploratory study in a rat model of traumatic brain injury"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7555-7431","authenticated-orcid":false,"given":"Bhanu Prakash","family":"KN","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arvind","family":"CS","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdalla","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna Kanth","family":"Chitta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan Vinh","family":"To","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hussein","family":"Srour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatima","family":"Nasrallah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"2752_CR1","unstructured":"Centers for Disease Control and Prevention. (2015). Report to congress on traumatic brain injury in the United States: epidemiology and rehabilitation. National Center for Injury Prevention and Control; Division of Unintentional Injury Prevention. Atlanta, GA"},{"key":"2752_CR2","unstructured":"Marr, A.L., and Coronado, V.G. (Eds.). (2004). Central nervous system injury surveillance data submission standards\u20142002. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control."},{"key":"2752_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/B978-0-444-52892-6.00004-0","volume":"127","author":"AC McKee","year":"2015","unstructured":"McKee AC, Daneshvar DH (2015) The neuropathology of traumatic brain injury. Handb Clin Neurol 127:45\u201366","journal-title":"Handb Clin Neurol"},{"key":"2752_CR4","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1016\/j.neuroscience.2004.06.046","volume":"129","author":"AW Unterberg","year":"2004","unstructured":"Unterberg AW, Stover J, Kress B, Kiening KL (2004) Edema and brain trauma. Neuroscience 129:1021\u20131029","journal-title":"Neuroscience"},{"issue":"6","key":"2752_CR5","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1148\/rg.2019190076","volume":"39","author":"AD Schweitzer","year":"2019","unstructured":"Schweitzer AD, Niogi SN, Whitlow CJ, Tsiouris AJ (2019) Traumatic brain injury imaging patterns and complications. Radiograph: Rev Pub Radiol Soc North Am Inc 39(6):1571\u20131595","journal-title":"Radiograph: Rev Pub Radiol Soc North Am Inc"},{"issue":"4","key":"2752_CR6","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1002\/nbm.2886","volume":"26","author":"L Christine Turtzo","year":"2013","unstructured":"Christine Turtzo L, Budde MD, Gold EM, Lewis BK, Janes L, Yarnell A, Grunberg NE, Watson W, Frank JA (2013) The evolution of traumatic brain injury in a rat focal contusion model. NMR Biomed. 26(4):468\u2013479. https:\/\/doi.org\/10.1002\/nbm.2886","journal-title":"NMR Biomed."},{"key":"2752_CR7","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0304-3940(02)00196-9","volume":"324","author":"M Stoffel","year":"2002","unstructured":"Stoffel M, Rinecker M, Graf R, Baethmann A, Plesnila N (2002) Nitric oxide in the penumbra of focal cortical necrosis in rats. Neurosci Lett 324:201\u2013204","journal-title":"Neurosci Lett"},{"issue":"9","key":"2752_CR8","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1097\/WNR.0000000000001213","volume":"30","author":"H Ren","year":"2019","unstructured":"Ren H, Lu H (2019) Dynamic features of brain edema in rat models of traumatic brain injury. NeuroReport. 30(9):605\u2013611. https:\/\/doi.org\/10.1097\/WNR.0000000000001213","journal-title":"NeuroReport."},{"issue":"1","key":"2752_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2014.12.003","volume":"21","author":"C Ledig","year":"2015","unstructured":"Ledig C, Heckemann RA, Hammers A, L\u00f3pez JC, Newcombe VF, Makropoulos A, L\u00f6tj\u00f6nen J, Menon DK, Rueckert D (2015) Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 21(1):40\u201358","journal-title":"Med Image Anal"},{"issue":"11","key":"2752_CR10","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1089\/neu.2011.1920","volume":"28","author":"A Irimia","year":"2011","unstructured":"Irimia A, Chambers MC, Alger JR, Filippou M, Prastawa M, Wang B, Hovda DA, Gerig G, Toga AW, Kikinis R, Vespa PM, Van Horn JD (2011) Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. J Neurotrauma 28(11):2287\u20132306","journal-title":"J Neurotrauma"},{"key":"2752_CR11","unstructured":"Smith CM (2017). Chapter 8 - Neurotrauma.\u00a0Handbook of clinical neurology, 145."},{"issue":"7","key":"2752_CR12","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1038\/jcbfm.2010.15","volume":"30","author":"R Immonen","year":"2010","unstructured":"Immonen R, Heikkinen T, T\u00e4htivaara L, Nurmi A, Stenius T-K, Puoliv\u00e4li J, Gr\u00f6hn O (2010) Cerebral blood volume alterations in the perilesional areas in the rat brain after traumatic brain injury\u2014comparison with behavioral outcome. J Cereb Blood Flow Metab 30(7):1318\u20131328. https:\/\/doi.org\/10.1038\/jcbfm.2010.15","journal-title":"J Cereb Blood Flow Metab"},{"key":"2752_CR13","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1089\/neu.2018.5972","volume":"36","author":"N Soni","year":"2019","unstructured":"Soni N, Mohamed AZ, Kurniawan ND, Borges K, Nasrallah F (2019) Diffusion magnetic resonance imaging unveils the spatiotemporal microstructural gray matter changes following injury in the rodent brain. J Neurotrauma 36:1306\u20131317. https:\/\/doi.org\/10.1089\/neu.2018.5972","journal-title":"J Neurotrauma"},{"key":"2752_CR14","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1089\/neu.2014.3563","volume":"32","author":"JA Long","year":"2015","unstructured":"Long JA, Watts LT, Chemello J, Huang S, Shen Q, Duong TQ (2015) Multiparametric and longitudinal MRI characterization of mild traumatic brain injury in rats. J Neurotrauma 32:598\u2013607. https:\/\/doi.org\/10.1089\/neu.2014.3563","journal-title":"J Neurotrauma"},{"key":"2752_CR15","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.nicl.2018.03.026","volume":"18","author":"JP Guenette","year":"2018","unstructured":"Guenette JP, Stern YorghosTripodis RA et al (2018) Automated versus manual segmentation of brain region volumes in former football players. NeruImage Clinical 18:888\u2013896","journal-title":"NeruImage Clinical"},{"key":"2752_CR16","doi-asserted-by":"crossref","unstructured":"Akkus Z, Galimzianova A, Hoogi A, Rubin DL & Erickson BJ (2017). Deep learning for brain MRI segmentation: state of the art and future directions. Journal of Digital Imaging.","DOI":"10.1007\/s10278-017-9983-4"},{"key":"2752_CR17","volume-title":"Segmentation of traumatic brain injuries with convolutional neural networks","author":"K Kamnitsas","year":"2015","unstructured":"Kamnitsas K, Ledig C, Newcombe V, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2015) Segmentation of traumatic brain injuries with convolutional neural networks"},{"key":"2752_CR18","volume-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. MICCAI"},{"key":"2752_CR19","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 VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61\u201378","journal-title":"Med Image Anal"},{"key":"2752_CR20","doi-asserted-by":"crossref","unstructured":"Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Rueckert D & Glocker B (2016). DeepMedic for brain tumor segmentation. BrainLes@MICCAI.","DOI":"10.1007\/978-3-319-55524-9_14"},{"key":"2752_CR21","unstructured":"Kayalibay B, Jensen G & Smagt PV (2017). CNN-based segmentation of medical imaging data.\u00a0ArXiv, abs\/1701.03056.."},{"key":"2752_CR22","doi-asserted-by":"publisher","first-page":"101787","DOI":"10.1016\/j.media.2020.101787","volume":"65","author":"G Wang","year":"2020","unstructured":"Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S (2020) Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal 65:101787","journal-title":"Med Image Anal"},{"key":"2752_CR23","doi-asserted-by":"crossref","unstructured":"Tureckova Alzbeta and A. Rodr\u00edguez-S\u00e1nchez.(2018). ISLES Challenge: U-shaped convolution neural network with dilated convolution for 3D stroke lesion segmentation.\u00a0BrainLes@MICCAI.","DOI":"10.1007\/978-3-030-11723-8_32"},{"key":"2752_CR24","volume-title":"U-ISLES: ischemic stroke lesion segmentation using U-Net","author":"LK Cornelio","year":"2018","unstructured":"Cornelio LK, Castillo MA, Naval P (2018) U-ISLES: ischemic stroke lesion segmentation using U-Net. IntelliSys"},{"key":"2752_CR25","doi-asserted-by":"crossref","unstructured":"Cai Y, Wu S, Zhao W, Li Z, Wu Z & Ji S (2018). Concussion classification via deep learning using whole-brain white matter fiber strains.\u00a0PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0197992"},{"key":"2752_CR26","volume-title":"Medical diagnosis using deep learning techniques: a research survey","author":"MM Azad","year":"2021","unstructured":"Azad MM, Ganapathy A, Vadlamudi S, Paruchuri H (2021) Medical diagnosis using deep learning techniques: a research survey"},{"key":"2752_CR27","doi-asserted-by":"crossref","unstructured":"Gravesteijn B, Nieboer D, Ercole A, Lingsma HF, Nelson D, Calster BV & Steyerberg E (2020). Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol.","DOI":"10.1016\/j.jclinepi.2020.03.005"},{"key":"2752_CR28","doi-asserted-by":"crossref","unstructured":"Gan C, Sun D, Qin K, Zhao H & Xiao F.(2020). Improved traumatic brain injury classification approach based on deep learning.\u00a02020 9th International Conference on Bioinformatics and Biomedical Science","DOI":"10.1145\/3431943.3432288"},{"key":"2752_CR29","doi-asserted-by":"crossref","unstructured":"Raj R, Luostarinen T, Pursiainen E, Posti J, Takala R, Bendel S, Konttila T & Korja M (2019). Machine learning-based dynamic mortality prediction after traumatic brain injury.\u00a0Scientific Reports, 9..","DOI":"10.1038\/s41598-019-53889-6"},{"key":"2752_CR30","first-page":"5","volume":"I","author":"M Cenek","year":"2018","unstructured":"Cenek M, Hu M, York G, Dahl S (2018) Survey of image processing techniques for brain pathology diagnosis: challenges and opportunities. Frontiers in Robotics and A I:5","journal-title":"Frontiers in Robotics and A"},{"issue":"6","key":"2752_CR31","doi-asserted-by":"publisher","first-page":"e314","DOI":"10.1016\/S2589-7500(20)30085-6","volume":"2","author":"M Monteiro","year":"2020","unstructured":"Monteiro M, Newcombe V, Mathieu F, Adatia K, Kamnitsas K, Ferrante E, Das T, Whitehouse D, Rueckert D, Menon D, Glocker B (2020) Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study. The Lancet Digital health 2(6):e314\u2013e322","journal-title":"The Lancet Digital health"},{"key":"2752_CR32","first-page":"7320","volume":"52","author":"A Phaphuangwittayakul","year":"2022","unstructured":"Phaphuangwittayakul A, Guo Y, Ying F, Dawod AY, Angkurawaranon S, Angkurawaranon C (2022) An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury. Applied Intelligence (Dordrecht, Netherlands) 52:7320\u20137338","journal-title":"Applied Intelligence (Dordrecht, Netherlands)"},{"key":"2752_CR33","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1016\/j.wneu.2022.02.097","volume":"164","author":"SM Adil","year":"2022","unstructured":"Adil SM, Elahi C, Patel DN, Seas A, Warman PI, Fuller AT, Haglund MM, Dunn TW (2022Aug) Deep learning to predict traumatic brain injury outcomes in the low-resource setting. World Neurosurg 164:e8\u2013e16. https:\/\/doi.org\/10.1016\/j.wneu.2022.02.097","journal-title":"World Neurosurg"},{"key":"2752_CR34","doi-asserted-by":"crossref","unstructured":"Farzaneh N, Williamson CA, Gryak J & Najarian K (2021). A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication.\u00a0NPJ Digital Medicine, 4.","DOI":"10.1038\/s41746-021-00445-0"},{"key":"2752_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P & Brox T (2015). U-Net: convolutional networks for biomedical image segmentation.\u00a0ArXiv, abs\/1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2752_CR36","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee MJ, Heinrich M, Misawa K, Mori K, McDonagh SG, Hammerla N, Kainz B, Glocker B & Rueckert D. (2018). Attention U-Net: learning where to look for the pancreas.\u00a0ArXiv, abs\/1804.03999."},{"key":"2752_CR37","volume-title":"A deep learning framework for brain extraction in humans and animals with traumatic brain injury 2018, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"S Roy","year":"2018","unstructured":"Roy S, Knutsen A, Korotco A, Bosomtwi A, Dardzinski B, Butman JA, DzungPham L (2018) A deep learning framework for brain extraction in humans and animals with traumatic brain injury 2018, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). D.C., USA, Washington"},{"key":"2752_CR38","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.jneumeth.2019.05.003","volume":"323","author":"R Feo","year":"2019","unstructured":"Feo R, Giove F (2019) Towards an efficient segmentation of small rodents brain a short critical review. J Neurosci Methods 323:82\u201389","journal-title":"J Neurosci Methods"},{"key":"2752_CR39","doi-asserted-by":"crossref","unstructured":"Mohamed AZ, Cumming P & Nasrallah FA (2021). Traumatic brain injury augurs ill for prolonged deficits in the brain\u2019s structural and functional integrity following controlled cortical impact injury. Sci Rep 11.","DOI":"10.1038\/s41598-021-00660-5"},{"key":"2752_CR40","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143\u2013155. https:\/\/doi.org\/10.1002\/hbm.10062","journal-title":"Hum Brain Mapp"},{"key":"2752_CR41","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.neuroimage.2009.06.039","volume":"48","author":"W Zheng","year":"2009","unstructured":"Zheng W, Chee M, Zagorodnov V (2009) Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3. Neuroimage 48:73\u201383","journal-title":"Neuroimage"},{"key":"2752_CR42","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/S1053-8119(02)91132-8","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825\u2013841. https:\/\/doi.org\/10.1016\/S1053-8119(02)91132-8","journal-title":"Neuroimage"},{"key":"2752_CR43","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda F, Sparks R & Ourselin S (2020). TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning.\u00a0ArXiv, abs\/2003.04696.","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"2752_CR44","doi-asserted-by":"crossref","unstructured":"Muhammed K\u00fcr\u015fad U\u00e7ar,\u00a0Majid Nour,\u00a0Hatem Sindi,Kemal Polat(2020). The effect of training and testing process on machine learning in biomedical datasets. Mathematical Problems in Engineering. 2020 2836236","DOI":"10.1155\/2020\/2836236"},{"key":"2752_CR45","unstructured":"Zenoda website - https:\/\/zenodo.org"},{"key":"2752_CR46","doi-asserted-by":"publisher","first-page":"9962","DOI":"10.1523\/JNEUROSCI.1898-15.2016","volume":"36","author":"A Ert\u00fcrk","year":"2016","unstructured":"Ert\u00fcrk A, Mentz S, Stout EE, Hedehus M, Dominguez SL, Neumaier L, Krammer F, Llovera G, Srinivasan K, Hansen DV, Liesz A, Scearce-Levie K, Sheng M (2016) Interfering with the chronic immune response rescues chronic degeneration after traumatic brain injury. J Neurosci 36:9962\u20139975","journal-title":"J Neurosci"},{"key":"2752_CR47","doi-asserted-by":"crossref","unstructured":"Turtzo LC, Budde MD, Gold EM, Lewis BK, Janes L, Yarnell AM, Grunberg NE, Watson W & Frank JA (2013). The evolution of traumatic brain injury in a rat focal contusion model.\u00a0NMR in Biomedicine, 26.","DOI":"10.1002\/nbm.2886"},{"key":"2752_CR48","unstructured":"Sergey L, Szegedy C (2015, March 2). Batch normalization: accelerating deep network training by reducing internal covariate shift, from https:\/\/arxiv.org\/abs\/1502.03167."},{"key":"2752_CR49","unstructured":"Nair V, Hinton G (2010). Rectified linear units improve restricted boltzmann machines, from https:\/\/www.cs.toronto.edu\/~fritz\/absps\/reluICML.pdf"},{"key":"2752_CR50","doi-asserted-by":"crossref","unstructured":"Chen L, Zhu Y, Papandreou G, Schroff F & Adam H (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. ECCV.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2752_CR51","doi-asserted-by":"crossref","unstructured":"Islam M, Vibashan V, Jose VJ, Wijethilake N, Utkarsh U & Ren H (2019). Brain tumor segmentation and survival prediction using 3D attention UNet.\u00a0BrainLes@MICCAI","DOI":"10.1007\/978-3-030-46640-4_25"},{"key":"2752_CR52","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y & Wu J (2020). UNet 3+: a full-scale connected UNet for medical image segmentation.\u00a0ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1055\u20131059.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"2752_CR53","unstructured":"Loshchilov, Ilya, and Frank Hutter. (2017). Decoupled weight decay regularization.ICLR."},{"key":"2752_CR54","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"9","key":"2752_CR55","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher DP, Klanderman GA, Rucklidge WJ (Sept. 1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850\u2013863. https:\/\/doi.org\/10.1109\/34.232073","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2752_CR56","unstructured":"React JavaScript webpage https:\/\/reactjs.org\/"},{"issue":"4","key":"2752_CR57","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.2307\/2533516","volume":"53","author":"C Nickerson","year":"1997","unstructured":"Nickerson C (1997) A note on A concordance correlation coefficient to evaluate reproducibility. Biometrics 53(4):1503\u20131507","journal-title":"Biometrics"},{"issue":"2","key":"2752_CR58","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jneumeth.2006.09.007","volume":"160","author":"G Onyszchuk","year":"2007","unstructured":"Onyszchuk G, Al-Hafez B, He YY, Bilgen M, Berman NE, Brooks WM (2007) A mouse model of sensorimotor controlled cortical impact: characterization using longitudinal magnetic resonance imaging, behavioral assessments, and histology. J Neurosci Methods. 160(2):187\u201396","journal-title":"J Neurosci Methods."},{"issue":"2","key":"2752_CR59","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1089\/neu.2007.0430","volume":"25","author":"G Onyszchuk","year":"2008","unstructured":"Onyszchuk G, He YY, Berman NE, Brooks WM (2008) Detrimental effects of aging on outcome from traumatic brain injury: a behavioral, magnetic resonance imaging, and histological study in mice. J Neurotrauma 25(2):153\u201371","journal-title":"J Neurotrauma"},{"key":"2752_CR60","doi-asserted-by":"crossref","unstructured":"Hernandez-Ontiveros DG, Tajiri N, Acosta SA, Giunta BN, Tan J & Borlongan CV (2013). Microglia activation as a biomarker for traumatic brain injury.\u00a0Frontiers in Neurology, 4.","DOI":"10.3389\/fneur.2013.00030"},{"key":"2752_CR61","doi-asserted-by":"publisher","unstructured":"Roy Snehashis, Butman John, Chan Leighton, Pham Dzung. (2018). TBI contusion segmentation from MRI using convolutional neural networks. 158\u2013162. https:\/\/doi.org\/10.1109\/ISBI.2018.8363545.","DOI":"10.1109\/ISBI.2018.8363545"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02752-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02752-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02752-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T11:33:47Z","timestamp":1676288027000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02752-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["2752"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02752-4","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]},"assertion":[{"value":"7 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 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":"This study was approved by the Animal Research Ethics Committee (AEC) of the University of Queensland (IRB number: QBI\/036\/16\/MAIC).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}