{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:14:09Z","timestamp":1780485249226,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["YD2150002001"],"award-info":[{"award-number":["YD2150002001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Brain tumor segmentation using neural networks presents challenges in accurately capturing diverse tumor shapes and sizes while maintaining real-time performance. Additionally, addressing class imbalance is crucial for achieving accurate clinical results. To tackle these issues, this study proposes a novel N-shaped lightweight network that combines multiple feature pyramid paths and U-Net architectures. Furthermore, we ingeniously integrate hybrid attention mechanisms into various locations of depth-wise separable convolution module to improve efficiency, with channel attention found to be the most effective for skip connections in the proposed network. Moreover, we introduce a combination loss function that incorporates a newly designed weighted cross-entropy loss and dice loss to effectively tackle the issue of class imbalance. Extensive experiments are conducted on four publicly available datasets, i.e., UCSF-PDGM, BraTS 2021, BraTS 2019, and MSD Task 01 to evaluate the performance of different methods. The results demonstrate that the proposed network achieves superior segmentation accuracy compared to state-of-the-art methods. The proposed network not only improves the overall segmentation performance but also provides a favorable computational efficiency, making it a promising approach for clinical applications.<\/jats:p>","DOI":"10.3390\/e26020166","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T03:57:29Z","timestamp":1707969449000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An N-Shaped Lightweight Network with a Feature Pyramid and Hybrid Attention for Brain Tumor Segmentation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3538-5233","authenticated-orcid":false,"given":"Mengxian","family":"Chi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"An","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8953-0283","authenticated-orcid":false,"given":"Xu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenguo","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"},{"name":"State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China"},{"name":"Beijing Key Laboratory of Precision\/Ultra-Precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bauer, S., Wiest, R., Nolte, L.P., and Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol., 58.","DOI":"10.1088\/0031-9155\/58\/13\/R97"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.mri.2019.05.043","article-title":"A review on brain tumor segmentation of MRI images","volume":"61","author":"Wadhwa","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","article-title":"Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019","volume":"131","author":"Tiwari","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TST.2014.6961028","article-title":"A survey of MRI-based brain tumor segmentation methods","volume":"19","author":"Liu","year":"2014","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (1995). Self-Organizating Maps, Springer.","DOI":"10.1007\/978-3-642-97610-0"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jns.2015.10.032","article-title":"Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps","volume":"359","author":"Mei","year":"2015","journal-title":"J. Neurol. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.compmedimag.2007.04.004","article-title":"Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps","volume":"31","author":"Vijayakumar","year":"2007","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1007\/s10916-019-1416-0","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"43","author":"Thaha","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.irbm.2022.05.002","article-title":"A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods, Datasets, Libraries, and Future Directions","volume":"43","author":"Balwant","year":"2022","journal-title":"IRBM"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"020508","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical image segmentation based on U-Net: A review","volume":"64","author":"Du","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kermi, A., Mahmoudi, I., and Khadir, M.T. (2018, January 16). Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Revised Selected Papers, Part II.","DOI":"10.1007\/978-3-030-11726-9_4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Maurya, S., Kumar Yadav, V., Agarwal, S., and Singh, A. (2021, January 27). Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture. Proceedings of the International MICCAI Brainlesion Workshop, Online.","DOI":"10.1007\/978-3-031-09002-8_28"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1091850","DOI":"10.3389\/fpubh.2023.1091850","article-title":"MRI brain tumor segmentation using residual Spatial Pyramid Pooling-powered 3D U-Net","volume":"11","author":"Vijay","year":"2023","journal-title":"Front. Public Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119166","DOI":"10.1016\/j.eswa.2022.119166","article-title":"Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation","volume":"214","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2022.11.039","article-title":"Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion","volume":"520","author":"Deepak","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghosh, S., and Santosh, K. (2021, January 7\u20139). Tumor segmentation in brain MRI: U-Nets versus feature pyramid network. Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal.","DOI":"10.1109\/CBMS52027.2021.00013"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1080\/02564602.2021.1955760","article-title":"Encoder modified U-Net and feature pyramid network for multi-class segmentation of cardiac magnetic resonance images","volume":"39","author":"Sharan","year":"2022","journal-title":"IETE Tech. Rev."},{"key":"ref_19","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_20","unstructured":"Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J. (November, January 27). An empirical study of spatial attention mechanisms in deep networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rezaei-Dastjerdehei, M.R., Mijani, A., and Fatemizadeh, E. (2020, January 26\u201327). Addressing imbalance in multi-label classification using weighted cross entropy loss function. Proceedings of the 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran.","DOI":"10.1109\/ICBME51989.2020.9319440"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, R., Qian, B., Zhang, X., Li, Y., Wei, R., Liu, Y., and Pan, Y. (2020, January 17\u201320). Rethinking dice loss for medical image segmentation. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00094"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chang, J., Zhang, X., Ye, M., Huang, D., Wang, P., and Yao, C. (2018, January 13\u201315). Brain tumor segmentation based on 3D Unet with multi-class focal loss. Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI.2018.8633056"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). Unet++: A nested U-Net architecture for medical image segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Proceedings.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Myronenko, A. (2018, January 16). 3D MRI brain tumor segmentation using autoencoder regularization. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Revised Selected Papers, Part II.","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Futrega, M., Milesi, A., Marcinkiewicz, M., and Ribalta, P. (2021, January 27). Optimized U-Net for brain tumor segmentation. Proceedings of the International MICCAI Brainlesion Workshop, Online.","DOI":"10.1007\/978-3-031-09002-8_2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., and Xu, D. (2021, January 27). Swin UNETR: Swin transformers for semantic segmentation of brain tumors in mri images. Proceedings of the International MICCAI Brainlesion Workshop, Online.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maji, D., Sigedar, P., and Singh, M. (2022). Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomed. Signal Process. Control, 71.","DOI":"10.1016\/j.bspc.2021.103077"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., and Xu, D. (2022, January 4\u20138). UNETR: Transformers for 3d medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s11548-022-02738-5","article-title":"Multi-scale feature pyramid fusion network for medical image segmentation","volume":"18","author":"Zhang","year":"2023","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"nnFormer: Volumetric medical image segmentation via a 3D transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"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","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."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","article-title":"The medical segmentation decathlon","volume":"13","author":"Antonelli","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: A survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_39","unstructured":"Dvo\u0159\u00e1k, P., and Menze, B. (2015, January 9). Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. Proceedings of the Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2015, Held in Conjunction with MICCAI 2015, Munich, Germany. Revised Selected Papers."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sedlar, S. (2017, January 14). Brain tumor segmentation using a multi-path CNN based method. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada. Revised Selected Papers.","DOI":"10.1007\/978-3-319-75238-9_35"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, G., Li, W., Ourselin, S., and Vercauteren, T. (2017, January 14). Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada. Revised Selected Papers.","DOI":"10.1007\/978-3-319-75238-9_16"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gong, Y., Yu, X., Ding, Y., Peng, X., Zhao, J., and Han, Z. (2021, January 3\u20138). Effective fusion factor in FPN for tiny object detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00120"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, M., Wu, Y., and Wu, J. (2019, January 17). Aggregating multi-scale prediction based on 3D U-Net in brain tumor segmentation. Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China. Revised Selected Papers, Part I.","DOI":"10.1007\/978-3-030-46640-4_14"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chi, M., An, H., Jin, X., Wen, K., and Nie, Z. (2022, January 13\u201315). SCAR U-Net: A 3D Spatial-Channel Attention ResU-Net for Brain Tumor Segmentation. Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences, Amsterdam, The Netherlands.","DOI":"10.1145\/3570773.3570826"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fncom.2020.00009","article-title":"Segmenting brain tumor using cascaded V-Nets in multimodal MR images","volume":"14","author":"Hua","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Avesta, A., Hossain, S., Lin, M., Aboian, M., Krumholz, H.M., and Aneja, S. (2023). Comparing 3D, 2.5 D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020181"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101568","DOI":"10.1016\/j.media.2019.101568","article-title":"Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning","volume":"59","author":"Xu","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_48","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.imed.2022.07.002","article-title":"Transformers in medical image analysis","volume":"3","author":"He","year":"2023","journal-title":"Intell. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s11517-022-02746-2","article-title":"PneuNet: Deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer","volume":"61","author":"Wang","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jain, J., Singh, A., Orlov, N., Huang, Z., Li, J., Walton, S., and Shi, H. (2022, January 18\u201324). Semask: Semantically masked transformers for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, New Orleans, LA, USA.","DOI":"10.1109\/ICCVW60793.2023.00083"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yaqub, M., Feng, J., Zia, M.S., Arshid, K., Jia, K., Rehman, Z.U., and Mehmood, A. (2020). State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images. Brain Sci., 10.","DOI":"10.3390\/brainsci10070427"},{"key":"ref_53","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/2\/166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:59:54Z","timestamp":1760104794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/2\/166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["e26020166"],"URL":"https:\/\/doi.org\/10.3390\/e26020166","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]}}}