{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:29:59Z","timestamp":1760059799623,"version":"build-2065373602"},"reference-count":107,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordination for the Improvement of Higher Education Personnel (CAPES)","award":["88881.982645\/2004-01"],"award-info":[{"award-number":["88881.982645\/2004-01"]}]},{"name":"Foundation for Research Support and Scientific and Technological Development of Maranh\u00e3o (FAPEMA)","award":["88881.982645\/2004-01"],"award-info":[{"award-number":["88881.982645\/2004-01"]}]},{"name":"Foundation for Research Support of Piau\u00ed (FAPEPI)","award":["88881.982645\/2004-01"],"award-info":[{"award-number":["88881.982645\/2004-01"]}]},{"name":"Foundation of the State University of Piau\u00ed (UESPI)","award":["88881.982645\/2004-01"],"award-info":[{"award-number":["88881.982645\/2004-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Histological image analysis plays a crucial role in understanding and diagnosing various diseases, but manually segmenting these images is often complex, time-consuming, and heavily reliant on expert knowledge. Generative adversarial networks (GANs) have emerged as promising tools to assist in this task, enhancing the accuracy and efficiency of segmentation in histological images. This systematic literature review aims to explore how GANs have been utilized for segmentation in this field, highlighting the latest trends, key challenges, and opportunities for future research. The review was conducted across multiple digital libraries, including IEEE, Springer, Scopus, MDPI, and PubMed, with combinations of the keywords \u201cgenerative adversarial network\u201d or \u201cGAN\u201d, \u201csegmentation\u201d or \u201cimage segmentation\u201d or \u201csemantic segmentation\u201d, and \u201chistology\u201d or \u201chistological\u201d or \u201chistopathology\u201d or \u201chistopathological\u201d. We reviewed 41 GAN-based histological image segmentation articles published between December 2014 and February 2025. We summarized and analyzed these papers based on the segmentation regions, datasets, GAN tasks, segmentation tasks, and commonly used metrics. Additionally, we discussed advantages, challenges, and future research directions. The analyzed studies demonstrated the versatility of GANs in handling challenges like stain variability, multi-task segmentation, and data scarcity\u2014all crucial challenges in the analysis of histopathological images. Nevertheless, the field still faces important challenges, such as the need for standardized datasets, robust evaluation metrics, and better generalization across diverse tissues and conditions.<\/jats:p>","DOI":"10.3390\/app15147802","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T14:22:36Z","timestamp":1752243756000},"page":"7802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4143-8167","authenticated-orcid":false,"given":"Yanna Leidy Ketley Fernandes","family":"Cruz","sequence":"first","affiliation":[{"name":"Graduate Program in Electrical Engineering, Federal University of Maranh\u00e3o (UFMA), S\u00e3o Lu\u00eds 65080-805, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0410-0768","authenticated-orcid":false,"given":"Antonio Fhillipi Maciel","family":"Silva","sequence":"additional","affiliation":[{"name":"Computer Science Department, State University of Piau\u00ed (UESPI), Floriano 64800-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8894-5353","authenticated-orcid":false,"given":"Ewaldo Eder Carvalho","family":"Santana","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering, Federal University of Maranh\u00e3o (UFMA), S\u00e3o Lu\u00eds 65080-805, Brazil"},{"name":"Graduate Program in Computer and Systems Engineering, State University of Maranh\u00e3o (UEMA), S\u00e3o Lu\u00eds 65081-400, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3988-8476","authenticated-orcid":false,"given":"Daniel G.","family":"Costa","sequence":"additional","affiliation":[{"name":"SYSTEC-ARISE, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mezei, T., Kolcs\u00e1r, M., Jo\u00f3, A., and Gurzu, S. 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