Abstract
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.
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.
| Original language | English |
|---|---|
| Article number | 86 |
| Pages (from-to) | 1-19 |
| Number of pages | 19 |
| Journal | Bioengineering |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published (in print/issue) - 17 Jan 2024 |
Bibliographical note
Publisher Copyright:© 2024 by the authors.
Keywords
- stroke
- deep learning
- lesion segmentation
- network
- survey
