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Accurate detection and prognosis of this disease are critical to provide essential guidelines for treatment planning. This study proposed using a deep learning\u2010based network for the GBM segmentation and radiomic features for the patient's overall survival (OS) time prediction. The segmentation model used in this study was a modified U\u2010Net\u2010based deep 3D multi\u2010level dilated convolutional neural network. It uses multiple kernels of altered sizes to capture contextual information at different levels. The proposed scheme for OS time prediction overcomes the problem of information loss caused by the derivation of features in a single view due to the variation in the neighbouring pixels of the tumorous region. The selected features were based on texture, shape, and volume. These features were computed from the segmented components of tumour in axial, coronal, and sagittal views of magnetic resonance imaging slices. The proposed models were trained and evaluated on the BraTS 2019 dataset. Experimental results of OS time prediction on the validation data have showed an accuracy of 48.3%, with the mean squared error of 92\u2009599.598. On the validation data, the segmentation model achieved a mean dice similarity coefficient of 0.75, 0.89, and 0.80 for enhancing tumour, whole tumour, and tumour core, respectively. Future work is warranted to improve the overall performance of OS time prediction based on the findings in this study.<\/jats:p>","DOI":"10.1002\/ima.22549","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T21:47:33Z","timestamp":1611870453000},"page":"1519-1535","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi\u2010level<\/scp> dilated convolutional neural network for brain tumour segmentation and multi\u2010view<\/scp>\u2010based radiomics for overall survival prediction"],"prefix":"10.1002","volume":"31","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2248-7254","authenticated-orcid":false,"given":"Asra","family":"Rafi","sequence":"first","affiliation":[{"name":"Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence, Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3491-0195","authenticated-orcid":false,"given":"Tahir Mustafa","family":"Madni","sequence":"additional","affiliation":[{"name":"Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence, Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4515-0742","authenticated-orcid":false,"given":"Uzair Iqbal","family":"Janjua","sequence":"additional","affiliation":[{"name":"Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence, Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0208-9419","authenticated-orcid":false,"given":"Muhammad Junaid","family":"Ali","sequence":"additional","affiliation":[{"name":"Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence, Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan"}]},{"given":"Muhammad Naeem","family":"Abid","sequence":"additional","affiliation":[{"name":"Medical Imaging and Diagnostics Lab, National Centre of Artificial Intelligence, Department of Computer Science COMSATS University Islamabad (CUI) Islamabad Pakistan"}]}],"member":"311","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2019.102903"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2617030"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ncl.2019.08.004"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-016-1545-1"},{"key":"e_1_2_9_6_1","unstructured":"ValkovIV OvcharovME MladenovskiMN VasilevNV DuhlenskiII.High grade glioma surgery\u2013clinical aspects and prognosis."},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/55166688"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1166\/jctn.2019.8096"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3399\/bjgp17X691277"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00726"},{"key":"e_1_2_9_13_1","first-page":"101692","article-title":"Fully automatic brain tumor segmentation with deep learning\u2010based selective attention using overlapping patches and multi\u2010class weighted cross\u2010entropy","volume":"29","author":"Akil M","year":"2020","journal-title":"Med Image Anal"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2019.102592"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-018-1858-4"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.12.003"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.10.002"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"LopezMM VenturaJ.Dilated convolutions for brain tumor segmentation in MRI scans. 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