{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T14:07:28Z","timestamp":1775570848362,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"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>We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions. Our Transformer Dil-DenseUNet combines DenseNet, dilated convolutions, and Transformer blocks, each contributing unique strengths to enhance segmentation accuracy. The DenseNet component captures fine-grained details and global features by leveraging dense connections, improving both precision and feature reuse. The dilated convolutional blocks, placed before each DenseNet module, expand the receptive field, capturing broader contextual information essential for accurate segmentation. Additionally, the Transformer blocks within our architecture address CNN limitations in capturing long-range dependencies by modeling complex spatial relationships through multi-head self-attention mechanisms. We assess our model\u2019s performance on the Ischemic Stroke Lesion Segmentation Challenge 2015 (SISS 2015) and ISLES 2022 datasets. In the testing phase, the model achieves a Dice coefficient of 0.80 \u00b1 0.30 on SISS 2015 and 0.81 \u00b1 0.33 on ISLES 2022, surpassing the current state-of-the-art results on these datasets.<\/jats:p>","DOI":"10.3390\/jimaging10120304","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:25:33Z","timestamp":1732533933000},"page":"304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6081-7538","authenticated-orcid":false,"given":"Nesrine","family":"Jazzar","sequence":"first","affiliation":[{"name":"Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia"},{"name":"National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2378-6320","authenticated-orcid":false,"given":"Besma","family":"Mabrouk","sequence":"additional","affiliation":[{"name":"Research Laboratory: Advanced Technologies for Medicine and Signals ATMS, Department of Electrical and Computer Engineering, National Engineers School, University of Sfax, Sfax 3038, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0178-501X","authenticated-orcid":false,"given":"Ali","family":"Douik","sequence":"additional","affiliation":[{"name":"Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shan, P., Hu, H., Wang, J., Yu, X., Wang, L., Shen, F., Zheng, J., Chen, X., Zhang, P., and Wang, Y. (2024). Disease burden attributable to dietary risks in Africa, 1990\u20132040: A systematic analysis for the Global Burden of Disease Study 2021. Preprint.","DOI":"10.21203\/rs.3.rs-5181852\/v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e347","DOI":"10.1161\/CIR.0000000000001209","article-title":"2024 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association","volume":"149","author":"Martin","year":"2024","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A. (2015). Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Proceedings, Part III, Springer.","DOI":"10.1007\/978-3-319-24574-4"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s11760-020-01816-y","article-title":"Deep convolution feature aggregation: An application to diabetic retinopathy severity level prediction","volume":"15","author":"Bodapati","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_5","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."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00138-024-01542-2","article-title":"Medtransnet: Advanced gating transformer network for medical image classification","volume":"35","author":"Shaik","year":"2024","journal-title":"Mach. Vis. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11760-020-01828-8","article-title":"Fully Convolutional Network with Attention Modules for Semantic Segmentation","volume":"15","author":"Huang","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17324","DOI":"10.48084\/etasr.8484","article-title":"A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches","volume":"14","author":"Reddi","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","article-title":"ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI","volume":"35","author":"Maier","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1038\/s41597-022-01875-5","article-title":"ISLES 2022: A multi-center Magnetic Resonance Imaging stroke lesion segmentation dataset","volume":"9","author":"Hanning","year":"2022","journal-title":"Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s00138-019-01042-8","article-title":"Utilization of DenseNet201 for diagnosis of breast abnormality","volume":"30","author":"Yu","year":"2019","journal-title":"Mach. Vis. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1109\/TMI.2017.2769839","article-title":"Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions","volume":"37","author":"Lessmann","year":"2017","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s00138-023-01418-x","article-title":"A high-level feature channel attention UNet network for cholangiocarcinoma segmentation from microscopy hyperspectral images","volume":"34","author":"Gao","year":"2023","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101791","DOI":"10.1016\/j.media.2020.101791","article-title":"Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease","volume":"65","author":"Liu","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kumar, A., Upadhyay, N., Ghosal, P., Chowdhury, T., Das, D., Mukherjee, A., and Nandi, D. (2020). CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. Comput. Methods Programs Biomed., 193.","DOI":"10.1016\/j.cmpb.2020.105524"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Albert, A., Cl\u00e8rigues, M., Valverde, S., Bernal, J., Freixenet, J., Oliver, A., and Llado, X. (2020). Acute and sub-acute stroke lesion segmentation from multimodal MRI. Comput. Methods Programs Biomed., 194.","DOI":"10.1016\/j.cmpb.2020.105521"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104865","DOI":"10.1016\/j.imavis.2023.104865","article-title":"A few-shot learning-based ischemic stroke segmentation system using weighted MRI fusion","volume":"140","author":"Alshehri","year":"2023","journal-title":"Image Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1049\/ell2.12444","article-title":"METrans: Multi-encoder transformer for ischemic stroke segmentation","volume":"58","author":"Wang","year":"2022","journal-title":"Electron. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120637","DOI":"10.1016\/j.eswa.2023.120637","article-title":"W-Net: A boundary-enhanced segmentation network for stroke lesions","volume":"230","author":"Wu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dogru, D., Ozdemir, M.A., and Guren, O. (2024, January 26\u201330). Deep Learning for Automated Ischemic Stroke Lesion Segmentation from Multi-spectral MRI. Proceedings of the EUSIPCO, Lyon, France.","DOI":"10.23919\/EUSIPCO63174.2024.10715216"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s10462-024-10900-5","article-title":"Transformers-based architectures for stroke segmentation: A review","volume":"57","author":"Rashed","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2023.3289293","article-title":"Transformer meets remote sensing video detection and tracking: A comprehensive survey","volume":"16","author":"Jiao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_26","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_29","first-page":"1","article-title":"Application of deep learning method on ischemic stroke lesion segmentation","volume":"1","author":"Zhang","year":"2022","journal-title":"J. Shanghai Jiaotong Univ. (Sci.)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/3699513","article-title":"Ischemic stroke segmentation by transformer and convolutional neural network using few-shot learning","volume":"20","author":"Alshehri","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/304\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:39:15Z","timestamp":1760114355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/12\/304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["jimaging10120304"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10120304","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,25]]}}}