A survey of MRI-based brain tissue segmentation using deep learning

Abstract

Segmentation of brain tissue from MR images provides detailed quantitative brain analysis for accurate diagnosis, detection, and classification of brain diseases, and plays an important role in neuroimaging research and clinical environments. Recently, a plethora of deep learning-based approaches have been employed to achieve brain tissue segmentation in fetuses, infants, and adults with impressive outcomes. However, owing to the existence of noise, motion artifacts, and edge blurriness in MR images, automatically segmenting brain tissue accurately from MR images is still a very challenging task. This survey examines both deep learning and MRI, providing an overview of the latest advances in fetal, infant, and adult brain tissue segmentation techniques based on deep learning. It includes the performance and quantitative analysis of the state-of-the-art methods. Over 100 scientific papers covering various technical aspects, including network architecture, prior knowledge, and attention mechanisms, were reviewed and analyzed. This article also comprehensively discusses these technologies and their potential applications in the future. Brain tissue segmentation provides detailed quantitative brain analysis for accurate diagnosis, detection, and classification of brain diseases, and plays an important role in neuroimaging research and clinical environments.