Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects. (December 2020) - Karthik, R.; Menaka, R.; Johnson, Annie; Anand, Sundar
Highlights: This research aims to emphasize the huge impact of deep learning models in brain stroke detection and lesion segmentation. The survey analyses 113 research papers published in different academic research databases in the past few years. The emerging trends and breakthroughs in stroke detection have been detailed in this evaluation. It concludes by examining the technical and non-technical challenges faced by researchers and the future implications in stroke detection. Abstract: Background and objective: 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. Methods: 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. Results: 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. Conclusion: 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.