Bennai, Mohamed TaharGuessoum, ZahiaMazouzi, SmaineCormier, StéphaneMezghiche, Mohamed2023-03-122023-03-12202301692607https://www.sciencedirect.com/science/article/pii/S0169260723001104https://doi.org/10.1016/j.cmpb.2023.107444https://dspace.univ-boumerdes.dz/handle/123456789/11172During the last decades, the healthcare area has increasingly relied on medical imaging for the diagnosis of a growing number of pathologies. The different types of medical images are mostly manually processed by human radiologists for diseases detection and monitoring. However, such a procedure is time-consuming and relies on expert judgment. The latter can be influenced by a variety of factors. One of the most complicated image processing tasks is image segmentation. Medical image segmentation consists of dividing the input image into a set of regions of interest, corresponding to body tissues and organs. Recently, artificial intelligence (AI) techniques brought researchers attention with their promising results for the image segmentation automation. Among AI-based techniques are those that use the Multi-Agent System (MAS) paradigm. This paper presents a comparative study of the multi-agent approaches dedicated to the segmentation of medical images, recently published in the literatureenImage segmentationMedical imagesMulti-agent systemsReviewSurveyArticle