{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:04:50Z","timestamp":1787169890387,"version":"build-2736575974"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:00:00Z","timestamp":1608681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072212"],"award-info":[{"award-number":["62072212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772227"],"award-info":[{"award-number":["61772227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81600923"],"award-info":[{"award-number":["81600923"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013060","name":"Jilin Provincial Health and Family Planning Commission","doi-asserted-by":"publisher","award":["2017J073"],"award-info":[{"award-number":["2017J073"]}],"id":[{"id":"10.13039\/501100013060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Magnetic resonance imaging (MRI) has a wide range of applications in medical imaging. Recently, studies based on deep learning algorithms have demonstrated powerful processing capabilities for medical imaging data. Previous studies have mostly focused on common diseases that usually have large scales of datasets and centralized the lesions in the brain. In this paper, we used deep learning models to process MRI images to differentiate the rare neuromyelitis optical spectrum disorder (NMOSD) from multiple sclerosis (MS) automatically, which are characterized by scattered and overlapping lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We proposed a novel model structure to capture 3D MRI images\u2019 essential information and converted them into lower dimensions. To empirically prove the efficiency of our model, firstly, we used a conventional 3-dimensional (3D) model to classify the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images and proved that the traditional 3D convolutional neural network (CNN) models lack the learning capacity to distinguish between NMOSD and MS. Then, we compressed the 3D T2-FLAIR images by a two-view compression block to apply two different depths (18 and 34 layers) of 2D models for disease diagnosis and also applied transfer learning by pre-training our model on ImageNet dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We found that our models possess superior performance when our models were pre-trained on ImageNet dataset, in which the models\u2019 average accuracies of 34 layers model and 18 layers model were 0.75 and 0.725, sensitivities were 0.707 and 0.708, and specificities were 0.759 and 0.719, respectively. Meanwhile, the traditional 3D CNN models lacked the learning capacity to distinguish between NMOSD and MS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The novel CNN model we proposed could automatically differentiate the rare NMOSD from MS, especially, our model showed better performance than traditional3D CNN models. It indicated that our 3D compressed CNN models are applicable in handling diseases with small-scale datasets and possess overlapping and scattered lesions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2020.612928","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T04:20:08Z","timestamp":1608697208000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["3D Compressed Convolutional Neural Network Differentiates Neuromyelitis Optical Spectrum Disorders From Multiple Sclerosis Using Automated White Matter Hyperintensities Segmentations"],"prefix":"10.3389","volume":"11","author":[{"given":"Zhuo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhezhou","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yishan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunjie","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2020,12,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1177\/0284185118795327","article-title":"Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer\u2019s disease imaging biomarker.","volume":"60","author":"Abrigo","year":"2019","journal-title":"Acta Radiol."},{"key":"B2","first-page":"1","article-title":"Scaling learning algorithms towards AI","author":"Bengio","year":"2007","journal-title":"Large-Scale Kernel Machines"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1109\/tnnls.2013.2293637","article-title":"On the complexity of neural network classifiers: a comparison between shallow and deep architectures.","volume":"25","author":"Bianchini","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"B4","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1002\/ana.25411","article-title":"Brain and cord imaging features in neuromyelitis optica spectrum disorders.","volume":"85","author":"Cacciaguerra","year":"2019","journal-title":"Ann. Neurol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1212\/wnl.0000000000003395","article-title":"Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest.","volume":"87","author":"Eshaghi","year":"2016","journal-title":"Neurology"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00679","article-title":"Intra-scanner and inter-scanner reproducibility of automatic white matter hyperintensities quantification.","volume":"13","author":"Guo","year":"2019","journal-title":"Front. Neurosci."},{"key":"B7","first-page":"3154","article-title":"Learning spatio-temporal features with 3d residual networks for action recognition","author":"Hara","year":"2017","journal-title":"Proceedings of the International Conference on Computer Vision and Pattern Recognition"},{"key":"B8","first-page":"6546","article-title":"Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet","author":"Hara","year":"2018","journal-title":"Proceedings of the International Conference on Computer Vision and Pattern Recognition"},{"key":"B9","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proceedings of the International Conference on Computer Vision and Pattern Recognition"},{"key":"B10","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1212\/wnl.0b013e31826845fe","article-title":"Does natalizumab therapy worsen neuromyelitis optica?","volume":"79","author":"Jacob","year":"2012","journal-title":"Neurology"},{"key":"B12","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1212\/WNL.0000000000001367","article-title":"MRI characteristics of neuromyelitis optica spectrum disorder.","volume":"84","author":"Kim","year":"2015","journal-title":"Neurology"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1177\/1352458512439439","article-title":"Does interferon beta treatment exacerbate neuromyelitis optica spectrum disorder?","volume":"18","author":"Kim","year":"2012","journal-title":"Mult. Scler. J."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1212\/wnl.33.11.1444","article-title":"Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).","volume":"33","author":"Kurtzke","year":"1983","journal-title":"Neurology"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3390\/genes9080382","article-title":"Multimodal 3D densenet for IDH genotype prediction in gliomas.","volume":"9","author":"Liang","year":"2018","journal-title":"Genes"},{"key":"B16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis.","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"B17","article-title":"Automate the quantitative calculation method of subregion brain atrophy. China Patent NO CN107103612B.","author":"Luo","year":"2017"},{"key":"B18","first-page":"565","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016","journal-title":"Proceeding of the International Conference on 3D Vision (3DV)"},{"key":"B19","first-page":"2924","article-title":"On the number of linear regions of deep neural networks","author":"Montufar","year":"2014","journal-title":"Proceedings of the International Conference on Neural Information Processing Systems"},{"key":"B20","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/978-3-319-46723-8_25","article-title":"3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients.","volume":"9901","author":"Nie","year":"2016","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"B21","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning.","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"B22","first-page":"355","article-title":"Predicting Alzheimer\u2019s disease: a neuroimaging study with 3D convolutional neural networks","author":"Payan","year":"2015","journal-title":"Proceedings of the International Conference on Pattern Recognition Applications and Methods"},{"key":"B23","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1002\/ana.22366","article-title":"Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria.","volume":"69","author":"Polman","year":"2011","journal-title":"Ann. Neurol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.patcog.2015.09.009","article-title":"Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images.","volume":"51","author":"Rastghalam","year":"2016","journal-title":"Pattern Recognit."},{"key":"B25","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"B26","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1007\/978-3-319-19992-4_46","article-title":"Multi-scale convolutional neural networks for lung nodule classification.","volume":"24","author":"Shen","year":"2015","journal-title":"Inf. Process. Med. Imaging"},{"key":"B27","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.jneumeth.2012.12.014","article-title":"Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction.","volume":"213","author":"Shi","year":"2013","journal-title":"J. Neurosci. Methods"},{"key":"B28","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting.","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/tmi.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B30","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/S1474-4422(17)30470-2","article-title":"Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria.","volume":"17","author":"Thompson","year":"2018","journal-title":"Lancet Neurol."},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101682","article-title":"Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry.","volume":"22","author":"Wang","year":"2019","journal-title":"Neuroimage Clin."},{"key":"B32","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.neucom.2018.12.018","article-title":"Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer\u2019s disease.","volume":"333","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"B33","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MSP.2010.936730","article-title":"Machine learning in medical imaging.","volume":"27","author":"Wernick","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"B34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1212\/WNL.0000000000001729","article-title":"International consensus diagnostic criteria for neuromyelitis optica spectrum disorders.","volume":"85","author":"Wingerchuk","year":"2015","journal-title":"Neurology"},{"key":"B35","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/S1474-4422(07)70216-8","article-title":"The spectrum of neuromyelitis optica.","volume":"6","author":"Wingerchuk","year":"2007","journal-title":"Lancet Neurol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1002\/mrm.22147","article-title":"Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.","volume":"62","author":"Zacharaki","year":"2009","journal-title":"Magn. Reson. Med."}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2020.612928\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T04:20:14Z","timestamp":1608697214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2020.612928\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,23]]},"references-count":36,"alternative-id":["10.3389\/fphys.2020.612928"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2020.612928","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,23]]},"article-number":"612928"}}