{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:06:50Z","timestamp":1780495610648,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The early and precise identification of a brain tumour is imperative for enhancing a patient\u2019s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.<\/jats:p>","DOI":"10.3390\/jimaging11010008","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T10:17:23Z","timestamp":1735899443000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhanced CATBraTS for Brain Tumour Semantic Segmentation"],"prefix":"10.3390","volume":"11","author":[{"given":"Rim","family":"El Badaoui","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ester","family":"Bonmati Coll","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Psarrou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hykoush A.","family":"Asaturyan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-0610","authenticated-orcid":false,"given":"Barbara","family":"Villarini","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, London W1W 6UW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref_1","unstructured":"Kortz, M.W., and Lillehei, K.O. (2024, July 31). Insular Cortex, Available online: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK570606\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1102\/1470-7330.2009.9003","article-title":"Tumor-like lesions of the brain","volume":"9","author":"Huisman","year":"2009","journal-title":"Cancer Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.amjmed.2017.12.039","article-title":"Brain Tumors","volume":"131","author":"Lee","year":"2018","journal-title":"Am. J. Med."},{"key":"ref_4","unstructured":"National Cancer Institute (2024, April 10). Adult Central Nervous System Tumors Treatment (PDQ\u00ae)\u2013Patient Version\u2014NCI, 2022, Available online: https:\/\/www.cancer.gov\/types\/brain\/patient\/adult-brain-treatment-pdq."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","article-title":"The 2021 WHO Classification of Tumors of the Central Nervous System: A summary","volume":"23","author":"Louis","year":"2021","journal-title":"Neuro-Oncology"},{"key":"ref_6","unstructured":"NHS England (2024, October 28). Survival by Cancer Group. Available online: https:\/\/digital.nhs.uk\/data-and-information\/publications\/statistical\/cancer-survival-in-england\/cancers-diagnosed-2015-to-2019-followed-up-to-2020\/survival-by-cancer-group."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"m4087","DOI":"10.1136\/bmj.m4087","article-title":"Mortality due to cancer treatment delay: Systematic review and meta-analysis","volume":"371","author":"Hanna","year":"2020","journal-title":"BMJ"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.acra.2015.05.007","article-title":"The Effects of Changes in Utilization and Technological Advancements of Cross-Sectional Imaging on Radiologist Workload","volume":"22","author":"McDonald","year":"2015","journal-title":"Acad. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Villarini, B., Asaturyan, H., Kurugol, S., Afacan, O., Bell, J.D., and Thomas, E.L. (2021, January 7\u20139). 3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities. Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Virtual Conference. ISSN 2372-9198.","DOI":"10.1109\/CBMS52027.2021.00066"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., and Chen, M. (2014, January 10\u201312). Medical image classification with convolutional neural network. Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1007\/s11063-022-10927-1","article-title":"An Improved VGG Model for Skin Cancer Detection","volume":"55","author":"Tabrizchi","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4828","DOI":"10.1038\/s41467-021-25138-w","article-title":"Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks","volume":"12","author":"Cen","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_15","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_16","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv, Available online: http:\/\/arxiv.org\/abs\/2010.11929."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201cHow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv, Available online: http:\/\/arxiv.org\/abs\/1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. arXiv, Available online: http:\/\/arxiv.org\/abs\/1411.4038.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Myronenko, A. (2018). 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. arXiv, Available online: http:\/\/arxiv.org\/abs\/1810.11654.","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv, Available online: http:\/\/arxiv.org\/abs\/1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., and Rozycki, M. (2019). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv, Available online: http:\/\/arxiv.org\/abs\/1811.02629."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and Its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H., and Xu, D. (2022). Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. arXiv, Available online: http:\/\/arxiv.org\/abs\/2103.14030.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S. (2018). nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. arXiv, Available online: http:\/\/arxiv.org\/abs\/1809.10486.","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"ref_27","unstructured":"Isensee, F., J\u00e4ger, P.F., Full, P.M., Vollmuth, P., and Maier-Hein, K.H. (2020). nnU-Net for Brain Tumor Segmentation. arXiv, Available online: http:\/\/arxiv.org\/abs\/2011.00848."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, C., Ding, M., Li, J., Yu, H., and Zha, S. (2021). TransBTS: Multimodal Brain Tumor Segmentation Using Transformer. arXiv, Available online: http:\/\/arxiv.org\/abs\/2103.04430.","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.13005\/bpj\/1484","article-title":"Noise Issues Prevailing in Various Types of Medical Images","volume":"11","author":"Goyal","year":"2018","journal-title":"Biomed. Pharmacol. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"El Badaoui, R., Coll, E.B., Psarrou, A., and Villarini, B. (2023, January 22\u201324). 3D CATBraTS: Channel Attention Transformer for Brain Tumour Semantic Segmentation. Proceedings of the 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L\u2019Aquila, Italy. ISSN 2372-9198.","DOI":"10.1109\/CBMS58004.2023.00267"},{"key":"ref_31","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014). Network in Network. arXiv, Available online: http:\/\/arxiv.org\/abs\/1312.4400."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Song, Z., Qiu, D., Zhao, X., Lin, D., and Hui, Y. (2023). Channel attention generative adversarial network for super-resolution of glioma magnetic resonance image. Comput. Methods Programs Biomed., 229.","DOI":"10.1016\/j.cmpb.2022.107255"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e220058","DOI":"10.1148\/ryai.220058","article-title":"The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset","volume":"4","author":"Calabrese","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., and Hess, C. (2023). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM). arXiv.","DOI":"10.1148\/ryai.220058"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_36","unstructured":"Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J.D., Flores Santamaria, N., Fathi Kazerooni, A., and Pati, S. (2023, December 23). Multi-Parametric Magnetic Resonance Imaging (mpMRI) Scans for De Novo Glioblastoma (GBM) Patients from the University of Pennsylvania Health System (UPENN-GBM). Available online: https:\/\/www.cancerimagingarchive.net\/collection\/upenn-gbm\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/s41597-022-01560-7","article-title":"The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics","volume":"9","author":"Bakas","year":"2022","journal-title":"Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107191","DOI":"10.1016\/j.dib.2021.107191","article-title":"The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma","volume":"37","author":"Incekara","year":"2021","journal-title":"Data Brief"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1385\/NI:5:1:11","article-title":"The extensible neuroimaging archive toolkit","volume":"5","author":"Marcus","year":"2007","journal-title":"Neuroinformatics"},{"key":"ref_40","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_41","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_42","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., and Pati, S. (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv, Available online: http:\/\/arxiv.org\/abs\/2107.02314."},{"key":"ref_43","unstructured":"Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., and Yang, D. (2022). MONAI: An Open-Source Framework for Deep Learning in Healthcare. arXiv, Available online: http:\/\/arxiv.org\/abs\/2211.02701."},{"key":"ref_44","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv, Available online: http:\/\/arxiv.org\/abs\/1608.03983."},{"key":"ref_45","unstructured":"Loshchilov, I., and Hutter, F. (2019). Decoupled Weight Decay Regularization. arXiv, Available online: http:\/\/arxiv.org\/abs\/1711.05101."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv, Available online: http:\/\/arxiv.org\/abs\/1707.01083.","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:22:37Z","timestamp":1759918957000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["jimaging11010008"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11010008","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,3]]}}}