{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:57:09Z","timestamp":1781593029078,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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>Segmentation of white blood cells is critical for a wide range of applications. It aims to identify and isolate individual white blood cells from medical images, enabling accurate diagnosis and monitoring of diseases. In the last decade, many researchers have focused on this task using U-Net, one of the most used deep learning architectures. To further enhance segmentation accuracy and robustness, recent advances have explored the combination of U-Net with other techniques, such as attention mechanisms and aggregation techniques. However, a common challenge in white blood cell image segmentation is the similarity between the cells\u2019 cytoplasm and other surrounding blood components, which often leads to inaccurate or incomplete segmentation due to difficulties in distinguishing low-contrast or subtle boundaries, leaving a significant gap for improvement. In this paper, we propose GAAD-U-Net, a novel architecture that integrates attention-augmented convolutions to better capture ambiguous boundaries and complex structures such as overlapping cells and low-contrast regions, followed by a gating mechanism to further suppress irrelevant feature information. These two key components are integrated in the Double U-Net base architecture. Our model achieves state-of-the-art performance on white blood cell benchmark datasets, with a 3.4% Dice score coefficient (DSC) improvement specifically on the SegPC-2021 dataset. The proposed model achieves superior performance as measured by mean the intersection over union (IoU) and DSC, with notably strong segmentation performance even for difficult images.<\/jats:p>","DOI":"10.3390\/jimaging11110386","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T14:40:48Z","timestamp":1762180848000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gated Attention-Augmented Double U-Net for White Blood Cell Segmentation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0958-4411","authenticated-orcid":false,"given":"Ilyes","family":"Benaissa","sequence":"first","affiliation":[{"name":"Laboratory of Vision Systems and Communication (VSC), Department of Electrical Engineering, University of Mohamed Khider Biskra, Biskra 07000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-8700","authenticated-orcid":false,"given":"Athmane","family":"Zitouni","sequence":"additional","affiliation":[{"name":"Laboratory of Vision Systems and Communication (VSC), Department of Electrical Engineering, University of Mohamed Khider Biskra, Biskra 07000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4256-8641","authenticated-orcid":false,"given":"Salim","family":"Sbaa","sequence":"additional","affiliation":[{"name":"Laboratory of Vision Systems and Communication (VSC), Department of Electrical Engineering, University of Mohamed Khider Biskra, Biskra 07000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-2247","authenticated-orcid":false,"given":"Nizamettin","family":"Aydin","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul 34485, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9262-6806","authenticated-orcid":false,"given":"Ahmed Chaouki","family":"Megherbi","sequence":"additional","affiliation":[{"name":"Laboratory of Identification, Command, Control and Communication (LI3C), Department of Electrical Engineering, University of Mohamed Khider, Biskra 07000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6876-2220","authenticated-orcid":false,"given":"Abdellah Zakaria","family":"Sellam","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle Ricerche (CNR), 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Laboratory of Institute of Electronics, Microelectronics and Nanotechnology (IEMN), UMR CNRS 8520, Universit\u00e9 Polytechnique Hauts-de-France, 59309 Valenciennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-2390","authenticated-orcid":false,"given":"Cosimo","family":"Distante","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle Ricerche (CNR), 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. 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