{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:33:17Z","timestamp":1767339197637,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder\u2013decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model\u2019s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.<\/jats:p>","DOI":"10.3390\/sym13020320","type":"journal-article","created":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T02:52:48Z","timestamp":1613443968000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8620-5039","authenticated-orcid":false,"given":"Yue","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}]},{"given":"Xiaoqiang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}]},{"given":"Kun","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Shandong University, Jinan 250353, China"}]},{"given":"Wentao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,16]]},"reference":[{"key":"ref_1","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., and Rozycki, M. (2018). Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (brats)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"170117","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s11548-020-02186-z","article-title":"Deepseg: Deep neural network framework for automatic brain tumor segmentation using magnetic resonance flair images","volume":"15","author":"Zeineldin","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.future.2020.05.027","article-title":"English text quality analysis based on recurrent neural network and semantic segmentation","volume":"112","author":"Luo","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101731","DOI":"10.1016\/j.media.2020.101731","article-title":"Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images","volume":"64","author":"Yuan","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115701","DOI":"10.1088\/1361-6501\/ab95db","article-title":"A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images","volume":"31","author":"Shuang","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with deep neural networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_10","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5\u20139 October 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, F., Ma, H., Sun, J., Wu, R., Liu, X., and Kong, Y. (2019, January 5\u20137). Lstm multi-modal unet for brain tumor segmentation. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981027"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/TITS.2019.2900426","article-title":"A 3D cnn-lstm-based image-to-image foreground segmentation","volume":"21","author":"Akilan","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101638","DOI":"10.1016\/j.media.2020.101638","article-title":"Deep atlas network for efficient 3D left ventricle segmentation on echocardiography","volume":"61","author":"Dong","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2019.02.006","article-title":"Obelisk-net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions","volume":"54","author":"Heinrich","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_16","first-page":"348","article-title":"On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task","volume":"Volume 10265","author":"Li","year":"2017","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, NC, USA, 25\u201330 June 2017"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.1109\/ACCESS.2019.2962608","article-title":"S 3 eganet: 3D spinal structures segmentation via adversarial nets","volume":"8","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"34029","DOI":"10.1109\/ACCESS.2020.2973707","article-title":"An encoder-decoder neural network with 3D squeeze-and-excitation and deep supervision for brain tumor segmentation","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., and Rueckert, D. (2017). Ensembles of multiple models and architectures for robust brain tumour segmentation. Lecture Notes in Computer Science, Proceedings of the International MICCAI Brainlesion Workshop, Quebec City, QC, Canada, 14 September 2017, Springer.","DOI":"10.1007\/978-3-319-75238-9_38"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D cnn with fully connected crf for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Myronenko, A. (2018). 3D mri brain tumor segmentation using autoencoder regularization. Lecture Notes in Computer Science, Proceedings of the International MICCAI Brainlesion Workshop, Granada, Spain, 16 September 2018, Springer.","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, C., Chen, S., Ding, C., and Tao, D. (2018). Learning contextual and attentive information for brain tumor segmentation. Lecture Notes in Computer Science, Proceedings of the International MICCAI Brainlesion Workshop, Granada, Spain, 16 September 2018, Springer.","DOI":"10.1007\/978-3-030-11726-9_44"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12252","DOI":"10.1109\/ACCESS.2019.2893496","article-title":"Adaptive independent subspace analysis (aisa) of brain magnetic resonance imaging (mri) data","volume":"7","author":"Ke","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Ashraf, I., Alhaisoni, M., Damaeviius, R., Scherer, R., Rehman, A., and Bukhari, S.A.C. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080565"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_26","first-page":"184","article-title":"3D dilated multi-fiber network for real-time brain tumor segmentation in mri","volume":"Volume 11766","author":"Chen","year":"2019","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China, 13\u201317 October 2019"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018). Multi-Fiber Networks for Video Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-030-01246-5_22"},{"key":"ref_28","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., and Davatzikos, C. (2017). Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch."},{"key":"ref_29","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., and Davatzikos, C. (2017). Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch., 286."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.patrec.2019.11.019","article-title":"Active deep neural network features selection for segmentation and recognition of brain tumors using mri images","volume":"129","author":"Sharif","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation, Springer International Publishing.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nuechterlein, N., and Mehta, S. (2019). 3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation, Springer International Publishing.","DOI":"10.1007\/978-3-030-11726-9_22"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cheng, J., Liu, J., Liu, L., Pan, Y., and Wang, J. (2019, January 18\u201321). Multi-level glioma segmentation using 3D u-net combined attention mechanism with atrous convolution. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983092"},{"key":"ref_35","unstructured":"Fang, L., and He, H. (2018, January 16\u201320). Three pathways u-net for brain tumor segmentation. Proceedings of the 7th Medical Image Computing and Computer-Assisted Interventions (MICCAI) BraTS Challenge, Granada, Spain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gates, E., Pauloski, J.G., Schellingerhout, D., and Fuentes, D. (2019). Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction, Springer International Publishing.","DOI":"10.1007\/978-3-030-11726-9_42"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu, X., Li, H., Zhao, Y., Dong, C., Menze, B.H., and Piraud, M. (2019). Hierarchical Multi-Class Segmentation of Glioma Images Using Networks with Multi-Level Activation Function, Springer International Publishing.","DOI":"10.1007\/978-3-030-11726-9_11"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lyu, C., and Shu, H. (2020). A two-stage cascade model with variational autoencoders and attention gates for mri brain tumor segmentation. arXiv.","DOI":"10.1007\/978-3-030-72084-1_39"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:24:35Z","timestamp":1760160275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/2\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,16]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["sym13020320"],"URL":"https:\/\/doi.org\/10.3390\/sym13020320","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,2,16]]}}}