{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T19:22:20Z","timestamp":1772133740618,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p> Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a novel deep convolutional neural network (CNN-Res) that automatically performs the segmentation of ischemic stroke lesions from multimodal MRIs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>CNN-Res used a U-shaped structure, so the network has encryption and decryption paths. The residual units are embedded in the encoder path. In this model, to reduce gradient descent, the residual units were used, and to extract more complex information in images, multimodal MRI data were applied. In the link between the encryption and decryption subnets, the bottleneck strategy was used, which reduced the number of parameters and training time compared to similar research.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>CNN-Res was evaluated on two distinct datasets. First, it was examined on a dataset collected from the Neuroscience Center of Tabriz University of Medical Sciences, where the average Dice coefficient was equal to 85.43%. Then, to compare the efficiency and performance of the model with other similar works, CNN-Res was evaluated on the popular SPES 2015 competition dataset where the average Dice coefficient was 79.23%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>This study presented a new and accurate method for the segmentation of MRI medical images using a deep convolutional neural network called CNN-Res, which directly predicts segment maps from raw input pixels.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02289-y","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T08:03:46Z","timestamp":1695715426000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images"],"prefix":"10.1186","volume":"23","author":[{"given":"Yousef","family":"Gheibi","sequence":"first","affiliation":[]},{"given":"Kimia","family":"Shirini","sequence":"additional","affiliation":[]},{"given":"Seyed Naser","family":"Razavi","sequence":"additional","affiliation":[]},{"given":"Mehdi","family":"Farhoudi","sequence":"additional","affiliation":[]},{"given":"Taha","family":"Samad-Soltani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"2289_CR1","doi-asserted-by":"crossref","unstructured":"Zhao B, Liu Z, Liu G, Cao C, Jin S, Wu H, et al. Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects. Comput Math Methods Med J. 2021;2021:3628179.","DOI":"10.1155\/2021\/3628179"},{"issue":"10260","key":"2289_CR2","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/S0140-6736(20)31374-X","volume":"396","author":"JD Pandian","year":"2020","unstructured":"Pandian JD, et al. Stroke systems of care in low-income and middle-income countries: challenges and opportunities. Lancet. 2020;396(10260):1443\u201351.","journal-title":"Lancet"},{"key":"2289_CR3","doi-asserted-by":"crossref","unstructured":"Zhang S, Zhang M, Ma S, Wang Q, Qu Y, Sun Z, et al. Research Progress of Deep Learning in the Diagnosis and Prevention of Stroke. BioMed Res Int. 2021;2021:5213550.","DOI":"10.1155\/2021\/5213550"},{"issue":"2","key":"2289_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1001\/jamaneurol.2020.4152","volume":"78","author":"VL Feigin","year":"2021","unstructured":"Collaborators GUND. Burden of Neurological Disorders Across the US From 1990-2017: A Global Burden of Disease Study. JAMA Neurology. 2021;78:165\u201376.","journal-title":"JAMA Neurol"},{"key":"2289_CR5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.vhri.2020.04.004","volume":"24","author":"MS Movahed","year":"2021","unstructured":"Movahed MS, Barghazan SH, Adel A, Rezapour A. Economic Burden of Stroke in Iran: A Population-Based Study. Value Health Regional Issues. 2021;24:77\u201381.","journal-title":"Value Health Regional Issues"},{"key":"2289_CR6","doi-asserted-by":"crossref","unstructured":"Lee, S.-H., Stroke Revisited: Pathophysiology of Stroke: From Bench to Bedside.\u00a0 Springer.\u00a02020","DOI":"10.1007\/978-981-10-1430-7"},{"issue":"4","key":"2289_CR7","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.emc.2017.07.007","volume":"35","author":"CR Cassella","year":"2017","unstructured":"Cassella CR, Jagoda A. Ischemic Stroke: Advances in Diagnosis and Management. Emerg Med Clin North Am. 2017;35(4):911\u201330.","journal-title":"Emerg Med Clin North Am"},{"key":"2289_CR8","doi-asserted-by":"crossref","unstructured":"Lin MP, Liebeskind DS. Imaging of Ischemic Stroke. Continuum (Minneapolis, Minn). 2016;22:1399-423.","DOI":"10.1212\/CON.0000000000000376"},{"issue":"5","key":"2289_CR9","doi-asserted-by":"publisher","first-page":"e218","DOI":"10.1002\/mp.13764","volume":"47","author":"HP Chan","year":"2020","unstructured":"Chan HP, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys. 2020;47(5):e218\u201327.","journal-title":"Med Phys"},{"issue":"1","key":"2289_CR10","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s12194-019-00552-4","volume":"13","author":"H Fujita","year":"2020","unstructured":"Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol. 2020;13(1):6\u201319.","journal-title":"Radiol Phys Technol"},{"issue":"1","key":"2289_CR11","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.bbe.2019.04.004","volume":"40","author":"A Subudhi","year":"2020","unstructured":"Subudhi A, Dash M, Sabut S. Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics Biomed Engineering. 2020;40(1):277\u201389.","journal-title":"Biocybernetics Biomed Engineering"},{"issue":"2","key":"2289_CR12","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1002\/jmri.27324","volume":"54","author":"SS Bhat","year":"2021","unstructured":"Bhat SS, et al. Low-field MRI of stroke: Challenges and opportunities. J Magn Reson Imaging. 2021;54(2):372\u201390.","journal-title":"J Magn Reson Imaging"},{"key":"2289_CR13","doi-asserted-by":"crossref","unstructured":"Jeena, R. and S. Kumar. A comparative analysis of MRI and CT brain images for stroke diagnosis. in 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy. IEEE.\u00a02013.","DOI":"10.1109\/AICERA-ICMiCR.2013.6575935"},{"key":"2289_CR14","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","volume":"35","author":"O Maier","year":"2017","unstructured":"Maier O, et al. ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal. 2017;35:250\u201369.","journal-title":"Med Image Anal"},{"key":"2289_CR15","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, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61\u201378.","journal-title":"Med Image Anal"},{"issue":"4","key":"2289_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00354-022-00172-4","volume":"40","author":"M Yildirim","year":"2022","unstructured":"Yildirim M, et al. COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers. New Gener Comput. 2022;40(4):1\u201315.","journal-title":"New Gener Comput"},{"issue":"1","key":"2289_CR17","first-page":"148","volume":"46","author":"T Samad-Soltani","year":"2017","unstructured":"Samad-Soltani T, Rezaei-Hachesu P, Ghazisaeedi M. Pervasive decision support systems in healthcare using intelligent robots in social media. Iran J Public Health. 2017;46(1):148\u20139.","journal-title":"Iran J Public Health"},{"issue":"5","key":"2289_CR18","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s00415-019-09518-3","volume":"268","author":"UK Patel","year":"2021","unstructured":"Patel UK, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268(5):1623\u201342.","journal-title":"J Neurol"},{"issue":"12","key":"2289_CR19","doi-asserted-by":"publisher","first-page":"e351","DOI":"10.1161\/STROKEAHA.120.031295","volume":"51","author":"L Ding","year":"2020","unstructured":"Ding L, Liu C, Li Z, Wang Y. Incorporating Artificial Intelligence Into Stroke Care and Research. Stroke. 2020;51(12):e351\u20134.","journal-title":"Stroke"},{"issue":"2","key":"2289_CR20","doi-asserted-by":"publisher","first-page":"22","DOI":"10.4018\/IJISSS.2018040102","volume":"10","author":"R Safdari","year":"2018","unstructured":"Safdari R, et al. Evaluation of Classification Algorithms vs Knowledge-Based Methods for Differential Diagnosis of Asthma in Iranian Patients. Int J Information Systems Service Sector. 2018;10(2):22\u201335.","journal-title":"Int J Information Systems Service Sector"},{"issue":"3","key":"2289_CR21","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1002\/ima.22683","volume":"32","author":"O Ero\u011flu","year":"2022","unstructured":"Ero\u011flu O, Yildirim M. Automatic detection of eardrum otoendoscopic images in patients with otitis media using hybrid-based deep models. Int J Imaging Syst Technol. 2022;32(3):717\u201327.","journal-title":"Int J Imaging Syst Technol"},{"issue":"1","key":"2289_CR22","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3174\/ajnr.A6883","volume":"42","author":"JE Soun","year":"2021","unstructured":"Soun JE, et al. Artificial Intelligence and Acute Stroke Imaging. Am J Neuroradiol. 2021;42(1):2\u201311.","journal-title":"Am J Neuroradiol"},{"key":"2289_CR23","unstructured":"Acharya, U.R., et al., Automatic detection of ischemic stroke using higher order spectra features in brain MRI images. Cognitive Systems Research, 2019."},{"key":"2289_CR24","doi-asserted-by":"publisher","first-page":"105728","DOI":"10.1016\/j.cmpb.2020.105728","volume":"197","author":"R Karthik","year":"2020","unstructured":"Karthik R, Menaka R, Johnson A, Anand S. Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects. Comput Methods Programs Biomed. 2020;197:105728.","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"2289_CR25","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/JBHI.2020.3028243","volume":"25","author":"L Li","year":"2020","unstructured":"Li L, et al. Deep learning for hemorrhagic lesion detection and segmentation on brain ct images. IEEE J Biomed Health Inform. 2020;25(5):1646\u201359.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"2289_CR26","doi-asserted-by":"publisher","first-page":"19389","DOI":"10.1038\/s41598-020-76459-7","volume":"10","author":"A Arab","year":"2020","unstructured":"Arab A, et al. A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT. Sci Rep. 2020;10(1):19389.","journal-title":"Sci Rep"},{"key":"2289_CR27","doi-asserted-by":"crossref","unstructured":"Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci. 2021;13:103\u201317.","DOI":"10.1007\/s12539-020-00403-6"},{"key":"2289_CR28","doi-asserted-by":"crossref","unstructured":"Cuocolo R, Stanzione A, Faletti R, Gatti M, Calleris G, Fornari A, et al. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol. 2021;31:7575\u201383.","DOI":"10.1007\/s00330-021-07856-3"},{"key":"2289_CR29","doi-asserted-by":"crossref","unstructured":"Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585:357\u201362.","DOI":"10.1038\/s41586-020-2649-2"},{"key":"2289_CR30","doi-asserted-by":"crossref","unstructured":"Aria, M., E. Nourani, and A. Golzari Oskouei, ADA-COVID: adversarial deep domain adaptation-based diagnosis of COVID-19 from lung CT scans using triplet embeddings. Comput Intell Neurosci. 2022;24:2022.","DOI":"10.1155\/2022\/2564022"},{"key":"2289_CR31","doi-asserted-by":"crossref","unstructured":"Suresh, H. and M. Niranjanamurthy. Image Processing Using OpenCV Technique for Real World Data. in International Conference on Innovative Computing and Cutting-edge Technologies. Springer.\u00a02020","DOI":"10.1007\/978-3-030-65407-8_24"},{"key":"2289_CR32","doi-asserted-by":"crossref","unstructured":"Liu, L., et al., A survey on U-shaped networks in medical image segmentations. 2020. 409: 244-258.","DOI":"10.1016\/j.neucom.2020.05.070"},{"key":"2289_CR33","doi-asserted-by":"crossref","unstructured":"Wu, Z., C. Shen, and A.J.P.R. Van Den Hengel, Wider or deeper: Revisiting the resnet model for visual recognition. 2019. 90:119-133.","DOI":"10.1016\/j.patcog.2019.01.006"},{"key":"2289_CR34","doi-asserted-by":"crossref","unstructured":"Lin, F., et al., Path aggregation U-Net model for brain tumor segmentation. 2021. 80(15): 22951-22964.","DOI":"10.1007\/s11042-020-08795-9"},{"key":"2289_CR35","doi-asserted-by":"crossref","unstructured":"Lin, C., et al., Real-time foreground object segmentation networks using long and short skip connections. 2021. 571: 543-559.","DOI":"10.1016\/j.ins.2021.01.044"},{"key":"2289_CR36","unstructured":"Chollet, F., Deep learning with Python. Simon and Schuster.\u00a02017"},{"key":"2289_CR37","doi-asserted-by":"crossref","unstructured":"Niazi, M.K.K., et al., Semantic segmentation to identify bladder layers from H&E Images. 2020. 15(1): 1-8.","DOI":"10.1186\/s13000-020-01002-1"},{"key":"2289_CR38","doi-asserted-by":"crossref","unstructured":"Liu L, Chen S, Zhang F, Wu F-X, Pan Y, Wang J. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. Neural Comput Appl. 2020;32:6545\u201358.","DOI":"10.1007\/s00521-019-04096-x"},{"key":"2289_CR39","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.neucom.2019.12.050","volume":"384","author":"L Liu","year":"2020","unstructured":"Liu L, et al. Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities. Neurocomputing. 2020;384:231\u201342.","journal-title":"Neurocomputing"},{"key":"2289_CR40","doi-asserted-by":"publisher","first-page":"101791","DOI":"10.1016\/j.media.2020.101791","volume":"65","author":"L Liu","year":"2020","unstructured":"Liu L, Kurgan L, Wu F-X, Wang J. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal. 2020;65:101791.","journal-title":"Med Image Anal"},{"key":"2289_CR41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in International Conference on Medical image computing and computer-assisted intervention. Springer.\u00a02015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"2289_CR42","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1139\/cjc-2017-0172","volume":"95","author":"Z Zhou","year":"2017","unstructured":"Zhou Z, et al. Nano-QSAR models for predicting cytotoxicity of metal oxide nanoparticles (MONPs) to E. coli. Canadian J Chemistry. 2017;95(8):863\u20136.","journal-title":"Canadian J Chemistry"},{"key":"2289_CR43","doi-asserted-by":"crossref","unstructured":"Long, J., E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2289_CR44","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-319-46976-8_19","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"M Drozdzal","year":"2016","unstructured":"Drozdzal M, et al. The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications. Springer; 2016. p. 179\u201387."},{"key":"2289_CR45","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al., Towards clinical diagnosis: automated stroke lesion segmentation on multimodal mr image using convolutional neural network. 2018.","DOI":"10.1109\/ACCESS.2018.2872939"},{"key":"2289_CR46","doi-asserted-by":"crossref","unstructured":"Drozdzal M, et al. Learning normalized inputs for iterative estimation in medical image segmentation. Comput Vision Pattern Recognit. 2018;44:1\u201313.","DOI":"10.1016\/j.media.2017.11.005"},{"key":"2289_CR47","doi-asserted-by":"crossref","unstructured":"Liu, L., et al., Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. 2019: 1\u201314.","DOI":"10.1007\/s00521-019-04096-x"},{"issue":"11","key":"2289_CR48","doi-asserted-by":"publisher","first-page":"6545","DOI":"10.1007\/s00521-019-04096-x","volume":"32","author":"L Liu","year":"2020","unstructured":"Liu L, et al. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. Neural Comput Appl. 2020;32(11):6545\u201358.","journal-title":"Neural Comput Appl"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02289-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02289-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02289-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T15:09:30Z","timestamp":1700492970000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02289-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2289"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02289-y","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]},"assertion":[{"value":"24 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 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 research resulted from a joint dissertation between the department of artificial intelligence at the University of Tabriz and the Neuroscience research center of Tabriz University of Medical Sciences with ethical approval code IR.TBZMED.REC.1399.039. The study was approved by the Tabriz University of Medical Sciences ethics committee. Data collection was carried out in accordance with the Deceleration of Helsinki. Informed consent from the patients was exempted by the Tabriz University of Medical Sciences ethics committee due to the retrospective nature of the study. Additional information such as demographics was removed from the images and then the brain object in all MRI slices was placed in the center of the image before annotation.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"192"}}