{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T19:46:52Z","timestamp":1789588012971,"version":"build-2803163510"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T00:00:00Z","timestamp":1593907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T00:00:00Z","timestamp":1593907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["EMCR 2015-7859"],"award-info":[{"award-number":["EMCR 2015-7859"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize pre-contrast T1-weighted (T1w),post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment,we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer\u2019s subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.<\/jats:p>","DOI":"10.1007\/s12021-020-09475-7","type":"journal-article","created":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T06:02:32Z","timestamp":1593928952000},"page":"159-184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6526-8126","authenticated-orcid":false,"given":"Sebastian R.","family":"van der Voort","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5563-2871","authenticated-orcid":false,"given":"Marion","family":"Smits","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4449-6784","authenticated-orcid":false,"given":"Stefan","family":"Klein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,5]]},"reference":[{"key":"9475_CR1","unstructured":"Abadi, M, Barham, P, Chen, J, Chen, Z, Davis, A, Dean, J, Devin, M, Ghemawat, S, Irving, G, Isard, M, Kudlur, M, Levenberg, J, Monga, R, Moore, S, Murray, DG, Steiner, B, Tucker, P, Vasudevan, V, Warden, P, Wicke, M, Yu, Y, & Zheng, X. (2016). TensorFlow: a system for large-scale machine learning. In 12th USENIX Symposium on operating systems design and implementation (OSDI 16). https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abad (pp. 265\u2013283): USENIX Association."},{"issue":"4","key":"9475_CR2","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s10278-017-9984-3","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z, Ali, I, Sedl\u00e1\u0159, J, Agrawal, JP, Parney, IF, Giannini, C, & Erickson, BJ. (2017). Predicting deletion of chromosomal arms 1p\/19q in low-grade gliomas from MR images using machine intelligence. Journal of Digital Imaging, 30(4), 469\u2013476. https:\/\/doi.org\/10.1007\/s10278-017-9984-3.","journal-title":"Journal of Digital Imaging"},{"key":"9475_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.cviu.2016.04.002","volume":"151","author":"J Arias","year":"2016","unstructured":"Arias, J, Mart\u00ednez-G\u00f3mez, J, G\u00e1mez, JA, de Herrera, AGS, & M\u00fcller, H. (2016). Medical image modality classification using discrete Bayesian networks. Computer Vision and Image Understanding, 151, 61\u201371. https:\/\/doi.org\/10.1016\/j.cviu.2016.04.002.","journal-title":"Computer Vision and Image Understanding"},{"key":"9475_CR4","doi-asserted-by":"publisher","unstructured":"Barboriak, D. (2015). Data from RIDER_NEURO_MRI. https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.VOSN3HN1.","DOI":"10.7937\/K9\/TCIA.2015.VOSN3HN1"},{"issue":"6","key":"9475_CR5","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K, Vendt, B, Smith, K, Freymann, J, Kirby, J, Koppel, P, Moore, S, Phillips, S, Maffitt, D, Pringle, M, Tarbox, L, & Prior, F. (2013). The cancer imaging archive (TCIA): maintaining and operating a public information repository. Journal of Digital Imaging, 26(6), 1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7.","journal-title":"Journal of Digital Imaging"},{"key":"9475_CR6","unstructured":"DeVries, T, & Taylor, GW. (2018). Learning confidence for out-of-distribution detection in neural networks. arXiv:180204865."},{"key":"9475_CR7","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.compmedimag.2014.06.005","volume":"39","author":"I Dimitrovski","year":"2015","unstructured":"Dimitrovski, I, Kocev, D, Kitanovski, I, Loskovska, S, & D\u017eeroski, S. (2015). Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics, 39, 14\u201326. https:\/\/doi.org\/10.1016\/j.compmedimag.2014.06.005.","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"9475_CR8","doi-asserted-by":"publisher","unstructured":"Erickson, B, Akkus, Z, Sedlar, J, & Korfiatis, P. (2016). Data from LGG-1p19qDeletion. https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.dwehtz9v.","DOI":"10.7937\/K9\/TCIA.2017.dwehtz9v"},{"issue":"9","key":"9475_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0184661","volume":"12","author":"O Esteban","year":"2017","unstructured":"Esteban, O, Birman, D, Schaer, M, Koyejo, OO, Poldrack, RA, & Gorgolewski, KJ. (2017). MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE, 12(9), 1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0184661.","journal-title":"PLOS ONE"},{"issue":"10","key":"9475_CR10","doi-asserted-by":"publisher","first-page":"e0164891:1","DOI":"10.1371\/journal.pone.0164891","volume":"11","author":"EH Fyllingen","year":"2016","unstructured":"Fyllingen, EH, Stensj\u00f8en, AL, Berntsen, EM, Solheim, O, & Reinertsen, I. (2016). Glioblastoma segmentation: comparison of three different software packages. PLOS ONE, 11(10), e0164891:1\u2013e0164891:16. https:\/\/doi.org\/10.1371\/journal.pone.0164891.","journal-title":"PLOS ONE"},{"key":"9475_CR11","unstructured":"Glorot, X, & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Teh, YW, & Titterington, M (Eds.) Proceedings of the thirteenth international conference on artificial intelligence and statistics, PMLR, proceedings of machine learning research. http:\/\/proceedings.mlr.press\/v9\/glorot10a.htm, (Vol. 9 pp. 249\u2013256)."},{"issue":"1","key":"9475_CR12","doi-asserted-by":"publisher","first-page":"160044:1","DOI":"10.1038\/sdata.2016.44","volume":"3","author":"KJ Gorgolewski","year":"2016","unstructured":"Gorgolewski, KJ, Auer, T, Calhoun, VD, Craddock, RC, Das, S, Duff, EP, Flandin, G, Ghosh, SS, Glatard, T, Halchenko, YO, Handwerker, DA, Hanke, M, Keator, D, Li, X, Michael, Z, Maumet, C, Nichols, BN, Nichols, TE, Pellman, J, Poline, JB, Rokem, A, Schaefer, G, Sochat, V, Triplett, W, Turner, JA, Varoquaux, G, & Poldrack, RA. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 160044:1\u2013160044:9. https:\/\/doi.org\/10.1038\/sdata.2016.44.","journal-title":"Scientific Data"},{"key":"9475_CR13","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.7490\/f1000research.1114354.1","volume":"6","author":"K Gorgolewski","year":"2017","unstructured":"Gorgolewski, K, Esteban, O, Schaefer, G, Wandell, B, & Poldrack, R. (2017). OpenNeuro \u2014 a free online platform for sharing and analysis of neuroimaging data. F1000research, 6, 1055. https:\/\/doi.org\/10.7490\/f1000research.1114354.1.","journal-title":"F1000research"},{"issue":"5","key":"9475_CR14","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","volume":"35","author":"H Greenspan","year":"2016","unstructured":"Greenspan, H, van Ginneken, B, & Summers, RM. (2016). Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153\u20131159. https:\/\/doi.org\/10.1109\/TMI.2016.2553401.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"9475_CR15","doi-asserted-by":"publisher","unstructured":"He, K, Zhang, X, Ren, S, & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on computer vision and pattern recognition (CVPR), (Vol. 29 pp. 770\u2013778), https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"9475_CR16","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s10278-015-9771-y","volume":"28","author":"JD Hirsch","year":"2015","unstructured":"Hirsch, JD, Siegel, EL, Balasubramanian, S, & Wang, KC. (2015). We built this house; it\u2019s time to move in: Leveraging existing DICOM structure to more completely utilize readily available detailed contrast administration information. Journal of Digital Imaging, 28(4), 407\u2013411. https:\/\/doi.org\/10.1007\/s10278-015-9771-y.","journal-title":"Journal of Digital Imaging"},{"key":"9475_CR17","doi-asserted-by":"publisher","unstructured":"Huang, G, Liu, Z, van der Maaten, L, & Weinberger, KQ. (2017). Densely connected convolutional networks. In 2017 IEEE Conference on computer vision and pattern recognition (CVPR), (Vol. 30 pp. 2261\u20132269), https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"2","key":"9475_CR18","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M, Beckmann, CF, Behrens, TE, Woolrich, MW, & Smith, SM. (2012). FSL. NeuroImage, 62(2), 782\u2013790. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.09.015.","journal-title":"NeuroImage"},{"key":"9475_CR19","unstructured":"Kingma, DP, & Ba, J. (2015). Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR conference track proceedings. ArXiv:1412.6980."},{"key":"9475_CR20","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A, Sutskever, I, & Hinton, GE. (2012). ImageNet classification with deep convolutional neural networks. In Pereira, F, Burges, CJ, Bottou, L, & Weinberger, KQ (Eds.) Communications of the ACM, (Vol. 25 pp. 1097\u20131105): Curran Associates, Inc., https:\/\/doi.org\/10.1145\/3065386.","DOI":"10.1145\/3065386"},{"issue":"12","key":"9475_CR21","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin, P, Leijenaar, RT, Deist, TM, Peerlings, J, de Jong, EE, van Timmeren, J, Sanduleanu, S, Larue, RT, Even, AJ, Jochems, A, van Wijk, Y, Woodruff, H, van Soest, J, Lustberg, T, Roelofs, E, van Elmpt, W, Dekker, A, Mottaghy, FM, Wildberger, JE, & Walsh, S. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12), 749\u2013762. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141.","journal-title":"Nature Reviews Clinical Oncology"},{"issue":"7","key":"9475_CR22","doi-asserted-by":"publisher","first-page":"P1097","DOI":"10.1016\/j.jalz.2018.06.1439","volume":"14","author":"PJ LaMontagne","year":"2018","unstructured":"LaMontagne, PJ, Keefe, S, Lauren, W, Xiong, C, Grant, EA, Moulder, KL, Morris, JC, Benzinger, TL, & Marcus, DS. (2018). OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer\u2019s disease. Alzheimer\u2019s & Dementia, 14(7), P1097. https:\/\/doi.org\/10.1016\/j.jalz.2018.06.1439.","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"9475_CR23","doi-asserted-by":"publisher","unstructured":"Lee, D, Kim, J, Moon, WJ, & Ye, JC. (2019). CollaGAN: collaborative GAN for missing image data imputation. In 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2019.00259 (pp. 2482\u20132491).","DOI":"10.1109\/CVPR.2019.00259"},{"key":"9475_CR24","doi-asserted-by":"publisher","unstructured":"Li, R, Zhang, W, Suk, HI, Wang, L, Li, J, Shen, D, & Ji, S. (2014). Deep learning based imaging data completion for improved brain disease diagnosis. In Golland, P, Hata, N, Barillot, C, Hornegger, J, & Howe, R (Eds.) Medical image computing and computer-assisted intervention \u2013 MICCAI 2014 (pp. 305\u2013312): Springer International Publishing, https:\/\/doi.org\/10.1007\/978-3-319-10443-0_39.","DOI":"10.1007\/978-3-319-10443-0_39"},{"key":"9475_CR25","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","volume":"264","author":"X Li","year":"2016","unstructured":"Li, X, Morgan, PS, Ashburner, J, Smith, J, & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of Neuroscience Methods, 264, 47\u201356. https:\/\/doi.org\/10.1016\/j.jneumeth.2016.03.001.","journal-title":"Journal of Neuroscience Methods"},{"issue":"1","key":"9475_CR26","doi-asserted-by":"publisher","first-page":"5467:1","DOI":"10.1038\/s41598-017-05848-2","volume":"7","author":"Z Li","year":"2017","unstructured":"Li, Z, Wang, Y, Yu, J, Guo, Y, & Cao, W. (2017). Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Scientific Reports, 7(1), 5467:1\u20135467:11. https:\/\/doi.org\/10.1038\/s41598-017-05848-2.","journal-title":"Scientific Reports"},{"issue":"2","key":"9475_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold, AS, & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift f\u00fcr Medizinische Physik, 29(2), 102\u2013127. https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002.","journal-title":"Zeitschrift f\u00fcr Medizinische Physik"},{"issue":"12","key":"9475_CR28","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1002\/acn3.644","volume":"5","author":"K Marek","year":"2018","unstructured":"Marek, K, Chowdhury, S, Siderowf, A, Lasch, S, Coffey, CS, Caspell-Garcia, C, Simuni, T, Jennings, D, Tanner, CM, Trojanowski, JQ, Shaw, LM, Seibyl, J, Schuff, N, Singleton, A, Kieburtz, K, Toga, AW, Mollenhauer, B, Galasko, D, Chahine, LM, Weintraub, D, Foroud, T, Tosun-Turgut, D, Poston, K, Arnedo, V, Frasier, M, Sherer, T, Bressman, S, Merchant, M, Poewe, W, Kopil, C, Naito, A, Dorsey, R, Casaceli, C, Daegele, N, Albani, J, Uribe, L, Foster, E, Long, J, Seedorff, N, Crawford, K, Smith, D, Casalin, P, Malferrari, G, Halter, C, Heathers, L, Russell, D, Factor, S, Hogarth, P, Amara, A, Hauser, R, Jankovic, J, Stern, M, Hu, SC, Todd, G, Saunders-Pullman, R, Richard, I, Saint-Hilaire, H, Seppi, K, Shill, H, Fernandez, H, Trenkwalder, C, Oertel, W, Berg, D, Brockman, K, Wurster, I, Rosenthal, L, Tai, Y, Pavese, N, Barone, P, Isaacson, S, Espay, A, Rowe, D, Brandabur, M, Tetrud, J, Liang, G, Iranzo, A, Tolosa, E, Marder, K, Sanchez, M, Stefanis, L, Marti, M, Martinez, J, Corvol, JC, Assly, O, Brillman, S, Giladi, N, Smejdir, D, Pelaggi, J, Kausar, F, Rees, L, Sommerfield, B, Cresswell, M, Blair, C, Williams, K, Zimmerman, G, Guthrie, S, Rawlins, A, Donharl, L, Hunter, C, Tran, B, Darin, A, Venkov, H, Thomas, CA, James, R, Heim, B, Deritis, P, Sprenger, F, Raymond, D, Willeke, D, Obradov, Z, Mule, J, Monahan, N, Gauss, K, Fontaine, D, Szpak, D, McCoy, A, Dunlop, B, Payne, L, Ainscough, S, Carvajal, L, Silverstein, R, Espay, K, Ranola, M, Rezola, E, Santana, H, Stamelou, M, Garrido, A, Carvalho, S, Kristiansen, G, Specketer, K, Mirlman, A, Facheris, M, Soares, H, Mintun, A, Cedarbaum, J, Taylor, P, Jennings, D, Slieker, L, McBride, B, Watson, C, Montagut, E, Sheikh, Z, Bingol, B, Forrat, R, Sardi, P, Fischer, T, Reith, D, Egebjerg, J, Larsen, L, Breysse, N, Meulien, D, Saba, B, Kiyasova, V, Min, C, McAvoy, T, Umek, R, Iredale, P, Edgerton, J, Santi, D, Czech, C, Boess, F, Sevigny, J, Kremer, T, Grachev, I, Merchant, K, Avbersek, A, Muglia, P, Stewart, A, Prashad, R, Taucher, J, & the Parkinson\u2019s Progression Markers Initiative. (2018). The parkinson\u2019s progression markers initiative (PPMI,) \u2013 establishing a PD biomarker cohort. Annals of Clinical and Translational Neurology, 5(12), 1460\u20131477. https:\/\/doi.org\/10.1002\/acn3.644.","journal-title":"Annals of Clinical and Translational Neurology"},{"issue":"1","key":"9475_CR29","doi-asserted-by":"publisher","first-page":"170010","DOI":"10.1038\/sdata.2017.10","volume":"4","author":"AD Martino","year":"2017","unstructured":"Martino, AD, O\u2019Connor, D, Chen, B, Alaerts, K, Anderson, JS, Assaf, M, Balsters, JH, Baxter, L, Beggiato, A, Bernaerts, S, Blanken, LME, Bookheimer, SY, Braden, BB, Byrge, L, Castellanos, FX, Dapretto, M, Delorme, R, Fair, DA, Fishman, I, Fitzgerald, J, Gallagher, L, Keehn, RJJ, Kennedy, DP, Lainhart, JE, Luna, B, Mostofsky, SH, M\u00fcller, RA, Nebel, MB, Nigg, JT, O\u2019Hearn, K, Solomon, M, Toro, R, Vaidya, CJ, Wenderoth, N, White, T, Craddock, RC, Lord, C, Leventhal, B, & Milham, MP. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data, 4(1), 170010. https:\/\/doi.org\/10.1038\/sdata.2017.10.","journal-title":"Scientific Data"},{"issue":"6 Part 1","key":"9475_CR30","doi-asserted-by":"publisher","first-page":"3253","DOI":"10.1118\/1.4709600","volume":"39","author":"L Mercier","year":"2012","unstructured":"Mercier, L, Maestro, RFD, Petrecca, K, Araujo, D, Haegelen, C, & Collins, DL. (2012). Online database of clinical MR and ultrasound images of brain tumors. Medical Physics, 39(6 Part 1), 3253\u20133261. https:\/\/doi.org\/10.1118\/1.4709600.","journal-title":"Medical Physics"},{"issue":"1","key":"9475_CR31","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s13244-019-0832-5","volume":"11","author":"E Montagnon","year":"2020","unstructured":"Montagnon, E, Cerny, M, Cadrin-Ch\u00eanevert, A, Hamilton, V, Derennes, T, Ilinca, A, Vandenbroucke-Menu, F, Turcotte, S, Kadoury, S, & Tang, A. (2020). Deep learning workflow in radiology: a primer. Insights into Imaging, 11(1), 22. https:\/\/doi.org\/10.1186\/s13244-019-0832-5.","journal-title":"Insights into Imaging"},{"issue":"3","key":"9475_CR32","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1148\/rg.2015140244","volume":"35","author":"SM Moore","year":"2015","unstructured":"Moore, SM, Maffitt, DR, Smith, KE, Kirby, JS, Clark, KW, Freymann, JB, Vendt, BA, Tarbox, LR, & Prior, FW. (2015). De-identification of medical images with retention of scientific research value. RadioGraphics, 35(3), 727\u2013735. https:\/\/doi.org\/10.1148\/rg.2015140244.","journal-title":"RadioGraphics"},{"key":"9475_CR33","doi-asserted-by":"publisher","unstructured":"National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). adiology data from the clinical proteomic tumor analysis consortium glioblastoma multiforme CPTAC-GBM collection. https:\/\/doi.org\/10.7937\/k9\/tcia.2018.3rje41q1.","DOI":"10.7937\/k9\/tcia.2018.3rje41q1"},{"key":"9475_CR34","doi-asserted-by":"publisher","unstructured":"Nie, D, Zhang, H, Adeli, E, Liu, L, & Shen, D. (2016). 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In Ourselin, S, Joskowicz, L, Sabuncu, MR, Unal, G, & Wells, W (Eds.) Medical image computing and computer-assisted intervention \u2013 MICCAI 2016, (Vol. 19 pp. 212\u2013220): Springer, https:\/\/doi.org\/10.1007\/978-3-319-46723-8_25.","DOI":"10.1007\/978-3-319-46723-8_25"},{"key":"9475_CR35","doi-asserted-by":"publisher","unstructured":"Pedano, N, Flanders, AE, Scarpace, L, Mikkelsen, T, Eschbacher, JM, Hermes, B, Sisneros, V, Barnholtz-Sloan, J, & Ostrom, Q. (2016). Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK.","DOI":"10.7937\/K9\/TCIA.2016.L4LTD3TK"},{"key":"9475_CR36","doi-asserted-by":"publisher","unstructured":"Pereira, S, Pinto, A, Alves, V, & Silva, CA. (2015). Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In Crimi, A, Menze, B, Maier, O, Reyes, M, & Handels, H (Eds.) Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, (Vol. 9556 pp. 131\u2013143): Springer, https:\/\/doi.org\/10.1007\/978-3-319-30858-6_12.","DOI":"10.1007\/978-3-319-30858-6_12"},{"key":"9475_CR37","doi-asserted-by":"publisher","unstructured":"Prasoon, A, Petersen, K, Igel, C, Lauze, F, Dam, E, & Nielsen, M. (2013). Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In Mori, K, Sakuma, I, Sato, Y, Barillot, C, & Navab, N (Eds.) Advanced information systems engineering (pp. 246\u2013253). Berlin: Springer, https:\/\/doi.org\/10.1007\/978-3-642-40763-5_31.","DOI":"10.1007\/978-3-642-40763-5_31"},{"issue":"1","key":"9475_CR38","doi-asserted-by":"publisher","first-page":"e180031","DOI":"10.1148\/ryai.2019180031","volume":"1","author":"LM Prevedello","year":"2019","unstructured":"Prevedello, LM, Halabi, SS, Shih, G, Wu, CC, Kohli, MD, Chokshi, FH, Erickson, BJ, Kalpathy-Cramer, J, Andriole, KP, & Flanders, AE. (2019). Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiology: Artificial Intelligence, 1(1), e180031. https:\/\/doi.org\/10.1148\/ryai.2019180031.","journal-title":"Radiology: Artificial Intelligence"},{"key":"9475_CR39","doi-asserted-by":"publisher","unstructured":"Remedios, S, Roy, S, Pham, DL, & Butman, JA. (2018). Classifying magnetic resonance image modalities with convolutional neural networks. In Petrick, N, & Mori, K (Eds.) Medical imaging 2018: computer-aided diagnosis, international society for optics and photonics, SPIE, (Vol. 10575 pp. 558\u2013563), https:\/\/doi.org\/10.1117\/12.2293943.","DOI":"10.1117\/12.2293943"},{"key":"9475_CR40","doi-asserted-by":"publisher","unstructured":"Scarpace, L, Flanders, AE, Jain, R, Mikkelsen, T, & Andrews, DW. (2015). Data from REMBRANDT. https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.588OZUZB.","DOI":"10.7937\/K9\/TCIA.2015.588OZUZB"},{"key":"9475_CR41","doi-asserted-by":"publisher","unstructured":"Scarpace, L, Mikkelsen, T, Cha, S, Rao, S, Tekchandani, S, Gutman, D, Saltz, JH, Erickson, BJ, Pedano, N, Flanders, AE, Barnholtz-Sloan, J, Ostrom, Q, Barboriak, D, & Pierce, LJ. (2016). Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9.","DOI":"10.7937\/K9\/TCIA.2016.RNYFUYE9"},{"key":"9475_CR42","doi-asserted-by":"publisher","unstructured":"Schmainda, K, & Prah, M. (2018). Data from brain-tumor-progression. https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.15quzvnb.","DOI":"10.7937\/K9\/TCIA.2018.15quzvnb"},{"key":"9475_CR43","doi-asserted-by":"publisher","unstructured":"Shah, N, Feng, X, Lankerovich, M, Puchalski, RB, & Keogh, B. (2016). Data from ivy GAP. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.XLwaN6nL.","DOI":"10.7937\/K9\/TCIA.2016.XLwaN6nL"},{"key":"9475_CR44","unstructured":"Simonyan, K, & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In Bengio, Y, & LeCun, Y (Eds.) International conference on learning representations, ICLR, conference track proceedings. https:\/\/dblp.org\/rec\/html\/journals\/corr\/SimonyanZ14a, Vol. 3."},{"key":"9475_CR45","unstructured":"Simonyan, K, Vedaldi, A, & Zisserman, A. (2014). Deep inside convolutional networks: visualising image classification models and saliency maps. In Bengio, Y, & LeCun, Y (Eds.) International conference on learning representations, ICLR, workshop track proceedings. arXiv:1312.6034."},{"key":"9475_CR46","unstructured":"Springenberg, JT, Dosovitskiy, A, Brox, T, & Riedmiller, MA. (2015). Striving for simplicity: the all convolutional net. In International conference on learning representations ICLR, workshop track proceedings. 1412.6806."},{"key":"9475_CR47","doi-asserted-by":"publisher","unstructured":"Srinivas, M, & Mohan, CK. (2014). Medical images modality classification using multi-scale dictionary learning. In 2014 19th International conference on digital signal processing, (Vol. 19 pp. 621\u2013625), https:\/\/doi.org\/10.1109\/ICDSP.2014.6900739.","DOI":"10.1109\/ICDSP.2014.6900739"},{"issue":"5","key":"9475_CR48","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N, Shin, JY, Gurudu, SR, Hurst, RT, Kendall, CB, Gotway, MB, & Liang, J. (2016). Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299\u20131312. https:\/\/doi.org\/10.1109\/TMI.2016.2535302.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"9475_CR49","doi-asserted-by":"publisher","first-page":"64","DOI":"10.2463\/mrms.mp.2018-0156","volume":"19","author":"D Tamada","year":"2020","unstructured":"Tamada, D, Kromrey, ML, Ichikawa, S, Onishi, H, & Motosugi, U. (2020). Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magnetic Resonance in Medical Sciences, 19(1), 64\u201376. https:\/\/doi.org\/10.2463\/mrms.mp.2018-0156.","journal-title":"Magnetic Resonance in Medical Sciences"},{"key":"9475_CR50","doi-asserted-by":"publisher","unstructured":"van Erp, TGM, Chervenak, AL, Kesselman, C, D\u2019Arcy, M, Sobell, J, Keator, D, Dahm, L, Murry, J, Law, M, Hasso, A, Ames, J, Macciardi, F, & Potkin, SG. (2011). Infrastructure for sharing standardized clinical brain scans across hospitals. In 2011 IEEE International conference on bioinformatics and biomedicine workshops (BIBMW) (pp. 1026\u20131028), https:\/\/doi.org\/10.1109\/BIBMW.2011.6112547.","DOI":"10.1109\/BIBMW.2011.6112547"},{"key":"9475_CR51","doi-asserted-by":"publisher","unstructured":"van Ooijen, PMA. (2019). Quality and curation of medical images and data. https:\/\/doi.org\/10.1007\/978-3-319-94878-2_17 (pp. 247\u2013255): Springer International Publishing.","DOI":"10.1007\/978-3-319-94878-2_17"},{"issue":"2","key":"9475_CR52","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10278-010-9329-y","volume":"24","author":"S Wang","year":"2011","unstructured":"Wang, S, Pavlicek, W, Roberts, CC, Langer, SG, Zhang, M, Hu, M, Morin, RL, Schueler, BA, Wellnitz, CV, & Wu, T. (2011). An automated DICOM database capable of arbitrary data mining (including radiation dose indicators) for quality monitoring. Journal of Digital Imaging, 24(2), 223\u2013233. https:\/\/doi.org\/10.1007\/s10278-010-9329-y.","journal-title":"Journal of Digital Imaging"},{"issue":"7","key":"9475_CR53","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.1002\/mp.12268","volume":"44","author":"Y Xiao","year":"2017","unstructured":"Xiao, Y, Fortin, M, Unsg\u00e5rd, G, Rivaz, H, & Reinertsen, I. (2017). REtroSpective evaluation of cerebral tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Medical Physics, 44(7), 3875\u20133882. https:\/\/doi.org\/10.1002\/mp.12268.","journal-title":"Medical Physics"},{"issue":"15","key":"9475_CR54","first-page":"5403","volume":"11","author":"Y Yu","year":"2015","unstructured":"Yu, Y, Lin, H, Yu, Q, Meng, J, Zhao, Z, Li, Y, & Zuo, L. (2015). Modality classification for medical images using multiple deep convolutional neural networks. Journal of Computational Information Systems, 11(15), 5403\u20135413.","journal-title":"Journal of Computational Information Systems"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-020-09475-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-020-09475-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-020-09475-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T23:03:48Z","timestamp":1625439828000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-020-09475-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,5]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9475"],"URL":"https:\/\/doi.org\/10.1007\/s12021-020-09475-7","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,5]]},"assertion":[{"value":"5 July 2020","order":1,"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":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}