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Most current models are based on this framework, customizing encoder\u2013decoder components to achieve higher accuracy across various segmentation challenges. However, this often comes at the cost of increased parameter counts, which inevitably limit their practicality in real-world applications. In this study, we provide an E-shaped segmentation framework that discards the traditional step-by-step resolution recovery decoding process, instead directly aggregating multi-scale features extracted by the encoder at each stage for deep cross-level integration. Additionally, we propose an innovative multi-scale large-kernel convolution (MLKConv) module, designed to enhance high-level feature representation by effectively capturing both local and global contextual information. Compared to U-structure, the proposed E-structured approach substantially reduces parameters while delivering superior performance, especially in complex segmentation tasks. Based on this structure, we develop 2 segmentation networks specifically for 2-dimensional (2D) and 3D medical images. 2D E-SegNet is evaluated on four 2D segmentation benchmark datasets (Synapse multi-organ, ACDC, Kvasir-Seg, and BUSI), while 3D E-SegNet is assessed on four 3D segmentation benchmark datasets (Synapse, ACDC, NIH Pancreas, and Lung). Experimental results demonstrate that our approach outperforms the current leading U-shaped models across multiple datasets, achieving new state-of-the-art (SOTA) performance with fewer parameters. In summary, our research introduces a novel approach to medical image segmentation, offering potential improvements and contributing to ongoing advancements in the field. Our code is publicly available on\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/zhaoqi106\/E-SegNet\">https:\/\/github.com\/zhaoqi106\/E-SegNet<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.34133\/research.0869","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T06:19:32Z","timestamp":1754979572000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":29,"title":["E-SegNet: E-Shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation"],"prefix":"10.34133","volume":"8","author":[{"given":"Wei","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Software Engineering, \rUniversity of Science and Technology Liaoning, Anshan 114051, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, \rUniversity of Science and Technology Liaoning, Anshan 114051, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenggui","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Data Science, \rJiaxing University, Jiaxing 314000, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ou","family":"Liu","sequence":"additional","affiliation":[{"name":"Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute, \rUniversity of Chinese Academy of Sciences, Wenzhou 325001, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9713-1864","authenticated-orcid":true,"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, \rUniversity of Science and Technology Liaoning, Anshan 114051, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-0544","authenticated-orcid":true,"given":"Jianwei","family":"Shuai","sequence":"additional","affiliation":[{"name":"Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute, \rUniversity of Chinese Academy of Sciences, Wenzhou 325001, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"issue":"12","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: The success of U-Net","volume":"46","author":"Azad R","year":"2022","unstructured":"Azad R, Aghdam EK, Rauland A, Jia Y, Avval AH, Bozorgpour A, Karimijafarbigloo S, Cohen JP, Adeli E, Merhof D. 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