Malik Mishaim, Chong Benjamin, Fernandez Justin, Shim Vickie, Kasabov Nikola Kirilov, Wang Alan
Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand.
Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand.
Bioengineering (Basel). 2024 Jan 17;11(1):86. doi: 10.3390/bioengineering11010086.
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.
中风是一种每年影响约1500万人的医学病症。患者及其家人可能面临严重的经济和情感挑战，因为中风会导致运动、言语、认知和情感障碍。中风病变分割在视觉上识别中风病变，同时提供有用的解剖学信息。虽然有不同的计算机辅助软件可用于手动分割，但最先进的深度学习使这项工作变得容易得多。这篇综述文章探讨了不同的基于深度学习的病变分割模型以及不同预处理技术对其性能的影响。它旨在全面概述最先进的模型，并旨在指导未来的研究，为开发更强大、更有效的中风病变分割模型做出贡献。
