Karthik R, Menaka R, Johnson Annie, Anand Sundar
Center for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Comput Methods Programs Biomed. 2020 Dec;197:105728. doi: 10.1016/j.cmpb.2020.105728. Epub 2020 Aug 26.
In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. This is achieved by discussing the state of the art approaches proposed by the recent works in this field.
In this study, the advancements in stroke lesion detection and segmentation were focused. The survey analyses 113 research papers published in different academic research databases. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation.
The features of the stroke lesion vary based on the type of imaging modality. To develop an effective method for stroke lesion detection, the features need to be carefully extracted from the input images. This review takes an attempt to categorize and discuss the different deep architectures employed for stroke lesion detection and segmentation, based on the underlying imaging modality. This further assists in understanding the relevance of the two-deep neural network components in medical image analysis namely Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN). It hints at other possible deep architectures that can be proposed for better results towards stroke lesion detection. Also, the emerging trends and breakthroughs in stroke detection have been detailed in this evaluation.
This work concludes by examining the technical and non-technical challenges faced by researchers and indicate the future implications in stroke detection. It could support the bio-medical researchers to propose better solutions for stroke lesion detection.
近年来，深度学习算法在应对不同领域的研究挑战方面产生了巨大影响。医学领域也从使用不断改进的深度学习模型中受益匪浅，这些模型节省时间并能产生准确的结果。本研究旨在强调深度学习模型在脑卒检测和病灶分割中的影响。这是通过讨论该领域近期研究提出的最新方法来实现的。
在本研究中，重点关注了脑卒中病灶检测与分割的进展。该综述分析了在不同学术研究数据库中发表的113篇研究论文。根据特定标准筛选出这些研究文章，以获得与脑卒中病灶检测和分割最相关的见解。
脑卒中病灶的特征因成像模态类型而异。为了开发一种有效的脑卒中病灶检测方法，需要从输入图像中仔细提取特征。本综述尝试根据基础成像模态对用于脑卒中病灶检测和分割的不同深度架构进行分类和讨论。这进一步有助于理解医学图像分析中两个深度神经网络组件即卷积神经网络（CNN）和全卷积网络（FCN）的相关性。它还暗示了为了在脑卒中病灶检测方面取得更好结果可以提出的其他可能的深度架构。此外，本评估详细介绍了脑卒中检测中的新兴趋势和突破。
这项工作通过审视研究人员面临的技术和非技术挑战来得出结论，并指出脑卒中检测的未来意义。它可以支持生物医学研究人员为脑卒中病灶检测提出更好的解决方案。
