{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:33:06Z","timestamp":1774369986046,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2016R1C1B2012433"],"award-info":[{"award-number":["2016R1C1B2012433"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017R1A2B1008020"],"award-info":[{"award-number":["2017R1A2B1008020"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s11548-019-02060-7","type":"journal-article","created":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T19:07:43Z","timestamp":1567537663000},"page":"151-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke"],"prefix":"10.1007","volume":"15","author":[{"given":"Minh Nguyen Nhat","family":"To","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Jeong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong Gee","family":"Roh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoon-Sik","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0287-4097","authenticated-orcid":false,"given":"Jin Tae","family":"Kwak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"issue":"3","key":"2060_CR1","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1161\/STROKEAHA.113.004072","volume":"45","author":"DS Liebeskind","year":"2014","unstructured":"Liebeskind DS, Tomsick TA, Foster LD, Yeatts SD, Carrozzella J, Demchuk AM, Jovin TG, Khatri P, von Kummer R, Sugg RM (2014) Collaterals at angiography and outcomes in the Interventional Management of Stroke (IMS) III trial. Stroke 45(3):759\u2013764","journal-title":"Stroke"},{"issue":"11","key":"2060_CR2","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1056\/NEJMoa1414905","volume":"372","author":"M Goyal","year":"2015","unstructured":"Goyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, Roy D, Jovin TG, Willinsky RA, Sapkota BL (2015) Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med 372(11):1019\u20131030","journal-title":"N Engl J Med"},{"issue":"6","key":"2060_CR3","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1136\/jnnp.2007.132100","volume":"79","author":"OY Bang","year":"2008","unstructured":"Bang OY, Saver JL, Buck BH, Alger JR, Starkman S, Ovbiagele B, Kim D, Jahan R, Duckwiler GR, Yoon SR (2008) Impact of collateral flow on tissue fate in acute ischaemic stroke. J Neurol Neurosurg Psychiatry 79(6):625\u2013629","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"2","key":"2060_CR4","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1148\/radiol.15142256","volume":"275","author":"BK Menon","year":"2015","unstructured":"Menon BK, d\u2019Esterre CD, Qazi EM, Almekhlafi M, Hahn L, Demchuk AM, Goyal M (2015) Multiphase CT angiography: a new tool for the imaging triage of patients with acute ischemic stroke. Radiology 275(2):510\u2013520","journal-title":"Radiology"},{"issue":"3\u20134","key":"2060_CR5","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1159\/000448525","volume":"5","author":"A Garcia-Tornel","year":"2016","unstructured":"Garcia-Tornel A, Carvalho V, Boned S, Flores A, Rodriguez-Luna D, Pagola J, Muchada M, Sanjuan E, Coscojuela P, Juega J, Rodriguez-Villatoro N, Menon B, Goyal M, Ribo M, Tomasello A, Molina CA, Rubiera M (2016) Improving the evaluation of collateral circulation by multiphase computed tomography angiography in acute stroke patients treated with endovascular reperfusion therapies. Interv Neurol 5(3\u20134):209\u2013217. https:\/\/doi.org\/10.1159\/000448525","journal-title":"Interv Neurol"},{"issue":"3","key":"2060_CR6","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1002\/ana.24211","volume":"76","author":"SJ Kim","year":"2014","unstructured":"Kim SJ, Son JP, Ryoo S, Lee MJ, Cha J, Kim KH, Kim GM, Chung CS, Lee KH, Jeon P (2014) A novel magnetic resonance imaging approach to collateral flow imaging in ischemic stroke. Ann Neurol 76(3):356\u2013369","journal-title":"Ann Neurol"},{"issue":"2","key":"2060_CR7","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1148\/radiol.10092333","volume":"257","author":"PM Robson","year":"2010","unstructured":"Robson PM, Dai W, Shankaranarayanan A, Rofsky NM, Alsop DC (2010) Time-resolved vessel-selective digital subtraction MR angiography of the cerebral vasculature with arterial spin labeling. Radiology 257(2):507\u2013515","journal-title":"Radiology"},{"issue":"9","key":"2060_CR8","doi-asserted-by":"publisher","first-page":"1640","DOI":"10.3174\/ajnr.A2564","volume":"32","author":"BK Menon","year":"2011","unstructured":"Menon BK, Smith EE, Modi J, Patel SK, Bhatia R, Watson TWJ, Hill MD, Demchuk AM, Goyal M (2011) Regional leptomeningeal score on CT angiography predicts clinical and imaging outcomes in patients with acute anterior circulation occlusions. Am J Neuroradiol 32(9):1640. https:\/\/doi.org\/10.3174\/ajnr.A2564","journal-title":"Am J Neuroradiol"},{"key":"2060_CR9","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A6068","author":"HG Roh","year":"2019","unstructured":"Roh HG, Kim EY, Kim IS, Lee HJ, Park JJ, Lee SB, Choi JW, Jeon YS, Park M, Kim SU, Kim HJ (2019) A novel collateral imaging method derived from time-resolved dynamic contrast-enhanced MR angiography in acute ischemic stroke: a pilot study. Am J Neuroradiol. https:\/\/doi.org\/10.3174\/ajnr.A6068","journal-title":"Am J Neuroradiol"},{"issue":"7553","key":"2060_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"key":"2060_CR11","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"2060_CR12","unstructured":"Lundervold AS, Lundervold A (2018) An overview of deep learning in medical imaging focusing on MRI. arXiv preprint arXiv:181110052"},{"key":"2060_CR13","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.1016\/j.acra.2018.02.018","volume":"25","author":"MP McBee","year":"2018","unstructured":"McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, Tridandapani S, Auffermann WF (2018) Deep learning in radiology. Acad Radiol 25:1472\u20131480","journal-title":"Acad Radiol"},{"key":"2060_CR14","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.artmed.2017.03.008","volume":"83","author":"C Sun","year":"2017","unstructured":"Sun C, Guo S, Zhang H, Li J, Chen M, Ma S, Jin L, Liu X, Li X, Qian X (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med 83:58\u201366","journal-title":"Artif Intell Med"},{"key":"2060_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"2060_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.cmpb.2019.03.007","volume":"173","author":"QD Vu","year":"2019","unstructured":"Vu QD, Kwak JT (2019) A dense multi-path decoder for tissue segmentation in histopathology images. Comput Methods Programs Biomed 173:119\u2013129","journal-title":"Comput Methods Programs Biomed"},{"key":"2060_CR17","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European conference on computer vision, 2016. Springer, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"2060_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11548-018-1764-0","volume":"13","author":"D Rav\u00ec","year":"2018","unstructured":"Rav\u00ec D, Szczotka AB, Shakir DI, Pereira SP, Vercauteren T (2018) Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction. Int J Comput Assist Radiol Surg 13:1\u20138","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2060_CR19","doi-asserted-by":"crossref","unstructured":"Garg R, BG VK, Carneiro G, Reid I (2016) Unsupervised cnn for single view depth estimation: geometry to the rescue. In: European conference on computer vision, 2016. Springer, pp 740\u2013756","DOI":"10.1007\/978-3-319-46484-8_45"},{"key":"2060_CR20","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.media.2018.06.005","volume":"48","author":"F Mahmood","year":"2018","unstructured":"Mahmood F, Durr NJ (2018) Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med Image Anal 48:230\u2013243","journal-title":"Med Image Anal"},{"key":"2060_CR21","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321\u2013331","journal-title":"Neurocomputing"},{"key":"2060_CR22","doi-asserted-by":"crossref","unstructured":"Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, I\u0161gum I (2017) Deep MR to CT synthesis using unpaired data. In: International workshop on simulation and synthesis in medical imaging, 2017. Springer, pp 14\u201323","DOI":"10.1007\/978-3-319-68127-6_2"},{"key":"2060_CR23","doi-asserted-by":"crossref","unstructured":"Hiasa Y, Otake Y, Takao M, Matsuoka T, Takashima K, Carass A, Prince JL, Sugano N, Sato Y (2018) Cross-modality image synthesis from unpaired data using CycleGAN. In: International workshop on simulation and synthesis in medical imaging, 2018. Springer, pp 31\u201341","DOI":"10.1007\/978-3-030-00536-8_4"},{"key":"2060_CR24","doi-asserted-by":"publisher","first-page":"989","DOI":"10.3389\/fneur.2018.00989","volume":"9","author":"C Lucas","year":"2018","unstructured":"Lucas C, Kemmling A, Bouteldja N, Aulmann LF, Mamlouk AM, Heinrich MP (2018) Learning to predict ischemic stroke growth on acute CT perfusion data by interpolating low-dimensional shape representations. Front Neurol 9:989","journal-title":"Front Neurol"},{"key":"2060_CR25","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.nicl.2017.06.016","volume":"15","author":"L Chen","year":"2017","unstructured":"Chen L, Bentley P, Rueckert D (2017) Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin 15:633\u2013643. https:\/\/doi.org\/10.1016\/j.nicl.2017.06.016","journal-title":"NeuroImage Clin"},{"key":"2060_CR26","doi-asserted-by":"crossref","unstructured":"Stier N, Vincent N, Liebeskind D, Scalzo F (2015) Deep learning of tissue fate features in acute ischemic stroke. In: 2015 IEEE international conference on bioinformatics and biomedicine (BIBM), 2015. IEEE, pp 1316\u20131321","DOI":"10.1109\/BIBM.2015.7359869"},{"key":"2060_CR27","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.3389\/fneur.2018.01060","volume":"9","author":"JAADS Pinto","year":"2018","unstructured":"Pinto JAADS, Mckinley R, Alves V, Wiest R, Silva CA, Reyes M (2018) Stroke lesion outcome prediction based on MRI imaging combined with clinical information. Front Neurol 9:1060","journal-title":"Front Neurol"},{"issue":"6","key":"2060_CR28","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1161\/STROKEAHA.117.019740","volume":"49","author":"A Nielsen","year":"2018","unstructured":"Nielsen A, Hansen Mikkel B, Tietze A, Mouridsen K (2018) Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke 49(6):1394\u20131401. https:\/\/doi.org\/10.1161\/STROKEAHA.117.019740","journal-title":"Stroke"},{"key":"2060_CR29","unstructured":"Robben D, Suetens P (2018) Perfusion parameter estimation using neural networks and data augmentation. In: International MICCAI brainlesion workshop, 2018. Springer, pp 439\u2013446"},{"key":"2060_CR30","unstructured":"Hess A, Meier R, Kaesmacher J, Jung S, Scalzo F, Liebeskind D, Wiest R, McKinley R (2018) Synthetic perfusion maps: imaging perfusion deficits in DSC-MRI with deep learning. In: International MICCAI brainlesion workshop, 2018. Springer, pp 447\u2013455"},{"key":"2060_CR31","doi-asserted-by":"crossref","unstructured":"Xiao Y, Alamer A, Fonov V, Lo BW, Tampieri D, Collins DL, Rivaz H, Kersten-Oertel M (2017) Towards automatic collateral circulation score evaluation in ischemic stroke using image decompositions and support vector machines. In: Jorge Cardoso M et al (eds) Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment. Springer, pp 158\u2013167","DOI":"10.1007\/978-3-319-67564-0_16"},{"key":"2060_CR32","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations"},{"key":"2060_CR33","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: Cortes C et al (eds) Advances in neural information processing systems, pp 2377\u20132385"},{"key":"2060_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2060_CR35","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, vol 2, p 3","DOI":"10.1109\/CVPR.2017.243"},{"key":"2060_CR36","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp 11\u201319","DOI":"10.1109\/CVPRW.2017.156"},{"key":"2060_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"issue":"12","key":"2060_CR38","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663\u20132674","journal-title":"IEEE Trans Med Imaging"},{"key":"2060_CR39","doi-asserted-by":"crossref","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks. In: Medical image computing and computer assisted intervention\u2014MICCAI 2018. Springer, Cham, pp 421\u2013429","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"2060_CR40","doi-asserted-by":"publisher","first-page":"422","DOI":"10.3389\/fnins.2019.00422","volume":"13","author":"S Wang","year":"2019","unstructured":"Wang S, Tang C, Sun J, Zhang Y (2019) Cerebral Micro-bleeding detection based on densely connected neural network. Front Neurosci 13:422. https:\/\/doi.org\/10.3389\/fnins.2019.00422","journal-title":"Front Neurosci"},{"issue":"11","key":"2060_CR41","doi-asserted-by":"publisher","first-page":"5129","DOI":"10.1002\/mp.13221","volume":"45","author":"Y Fu","year":"2018","unstructured":"Fu Y, Mazur TR, Wu X, Liu S, Chang X, Lu Y, Li HH, Kim H, Roach MC, Henke L (2018) A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy. Med Phys 45(11):5129\u20135137","journal-title":"Med Phys"},{"key":"2060_CR42","doi-asserted-by":"crossref","unstructured":"Yu L, Yang X, Chen H, Qin J, Heng P-A (2017) Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI, 2017, pp 66\u201372","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"2060_CR43","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"2060_CR44","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":"2060_CR45","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","volume":"170","author":"C Wachinger","year":"2018","unstructured":"Wachinger C, Reuter M, Klein T (2018) DeepNAT: deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170:434\u2013445","journal-title":"NeuroImage"},{"key":"2060_CR46","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2016.01.024","volume":"129","author":"J Kleesiek","year":"2016","unstructured":"Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A (2016) Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129:460\u2013469","journal-title":"NeuroImage"},{"key":"2060_CR47","volume-title":"Statistics at square one","author":"TDV Swinscow","year":"2002","unstructured":"Swinscow TDV, Campbell MJ (2002) Statistics at square one. BMJ, London"},{"issue":"1","key":"2060_CR48","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30(1):79\u201382","journal-title":"Clim Res"},{"key":"2060_CR49","volume-title":"Elementary mathematical theory of classification and prediction","author":"TT Tanimoto","year":"1958","unstructured":"Tanimoto TT (1958) Elementary mathematical theory of classification and prediction. IBM Corp., New York"},{"key":"2060_CR50","volume-title":"Pattern recognition","author":"S Theodoridis","year":"2008","unstructured":"Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, Inc, Cambridge","edition":"4"},{"issue":"4","key":"2060_CR51","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"2060_CR52","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, 2015. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2060_CR53","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5987\u20135995","DOI":"10.1109\/CVPR.2017.634"},{"key":"2060_CR54","unstructured":"Kinga D, Adam JB (2015) A method for stochastic optimization. In: International conference on learning representations (ICLR)"},{"key":"2060_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01261-8_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Yuxin Wu","year":"2018","unstructured":"Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), 2018, pp 3\u201319"},{"key":"2060_CR56","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Paper presented at the proceedings of the 32nd international conference on international conference on machine learning\u2014vol 37, Lille, France"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02060-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-019-02060-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02060-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T10:13:41Z","timestamp":1664273621000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-019-02060-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,3]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["2060"],"URL":"https:\/\/doi.org\/10.1007\/s11548-019-02060-7","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,3]]},"assertion":[{"value":"10 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}