Malik, MishaimChong, BenjaminFernandez, JustinShim, VickieKasabov, Nikola KirilovWang, Alan2024-02-012024-02-012024-01-17Bioengineering (Basel), ISSN: 2306-5354 (Print); 2306-5354 (Online), MDPI AG, 11(1), 86-. doi: 10.3390/bioengineering110100862306-53542306-5354http://hdl.handle.net/10292/17177Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/deep learninglesion segmentationnetworkstrokedeep learninglesion segmentationnetworkstroke40 Engineering4003 Biomedical EngineeringBehavioral and Social ScienceNetworking and Information Technology R&D (NITRD)NeurosciencesStrokeBrain DisordersRehabilitationStroke4003 Biomedical engineeringJournal ArticleOpenAccess10.3390/bioengineering11010086