{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T18:02:15Z","timestamp":1777917735239,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52474270"],"award-info":[{"award-number":["52474270"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. To address this challenge, this paper introduces a lightweight nuclear segmentation network called Attention-Enhanced U-Net (AttE-Unet) for cell segmentation. AttE-Unet enhances the network\u2019s feature extraction capabilities through an attention mechanism and combines the strengths of deep learning with traditional image filtering algorithms, while substantially reducing computational and storage demands. Experimental results on the PanNuke dataset demonstrate that AttE-Unet, despite its significant reduction in model size\u2014with the number of parameters and floating-point operations per second reduced to 1.57% and 0.1% of the original model, respectively\u2014still maintains a high level of segmentation performance. Specifically, the F1 score and Intersection over Union (IoU) score are 91.7% and 89.3% of the original model\u2019s scores. Furthermore, deployment on an MCU consumes only 2.09 MB of Flash and 1.38 MB of RAM, highlighting the model\u2019s lightweight nature and its potential for practical deployment as a medical image segmentation solution on edge devices.<\/jats:p>","DOI":"10.3390\/info16040295","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T05:59:00Z","timestamp":1744091940000},"page":"295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement"],"prefix":"10.3390","volume":"16","author":[{"given":"Shuang","family":"Xia","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 211116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3128-7264","authenticated-orcid":false,"given":"Qian","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyuxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoxing","family":"You","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1379-591X","authenticated-orcid":false,"given":"Kainan","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. 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