Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review

Malik, Mishaim
Chong, Benjamin
Fernandez, Justin
Shim, Vickie
Kasabov, Nikola Kirilov
Wang, Alan
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Stroke 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.

deep learning , lesion segmentation , network , stroke , deep learning , lesion segmentation , network , stroke , 40 Engineering , 4003 Biomedical Engineering , Behavioral and Social Science , Networking and Information Technology R&D (NITRD) , Neurosciences , Stroke , Brain Disorders , Rehabilitation , Stroke , 4003 Biomedical engineering
Bioengineering (Basel), ISSN: 2306-5354 (Print); 2306-5354 (Online), MDPI AG, 11(1), 86-. doi: 10.3390/bioengineering11010086
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