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.

Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.