The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

With the rapid development of synthetic aperture radar (SAR) technology, SAR remote sensing has a wide range of applications in fields such as marine surveillance and sea rescue. Currently, the SAR ship detection model based on deep learning suffers from the problems of low detection in real-time and low detection accuracy. In order to solve the above problems, this paper proposes a lightweight SAR ship detection network (EGTB-Net) based on transformer and feature enhancement. Firstly, we design a novel Ghost-ECA model as the backbone network of EGTB-Net, which reduces the number of parameters of the model and enhances the ability to identify key feature information at the same time. Then, we incorporate the transformer block in the backbone network to capture long-range dependencies, enrich contextual information, and improve the network's ability to capture different types of local information. Finally, we adopt a new SIoU loss function, which is used to solve the direction problem of mismatch between the real frame and the predicted frame and improve the network's ability to localize ship targets. The experimental results on the SAR-Ship-Dataset show that the mean average precision (mAP) of the method is 94.83%, the detection speed is 61 FPS, and the model size is only 5.94 M, while the model has excellent anti-interference ability.

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.

Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D‐Parallel‐RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground‐truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1‐weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D‐Parallel‐RicianNet network achieves performance superior to that of several state‐of‐the‐art methods in terms of the peak signal‐to‐noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

Alzheimer's disease (AD) is a degenerative mental disorder of the central nervous system that affects people's ability of daily life. Unfortunately, there is currently no known cure for AD. Thus, the early detection of AD plays a key role in preventing and controlling its progression. Magnetic resonance imaging (MRI)-based measures of cerebral atrophy are regarded as valid markers of the AD state. As one of representative methods for measuring brain atrophy, image registration technique has been widely adopted for AD diagnosis. However, AD detection is sensitive to the accuracy of image registration. To address this problem, an AD assistant diagnosis framework based on joint registration and classification is proposed. Specifically, in order to capture more local deformation information, we propose a novel patch-based joint brain image registration and classification network (RClaNet) to estimate the local dense deformation fields (DDF) and disease risk probability maps that explain high-risk areas for AD patients. RClaNet consists of a registration network and a classification network, in which the deformation field from registration network is fed into the classification network to enhance the prediction accuracy of the disease. Then, the exponential distance weighting method is used to obtain the global DDF and the global disease risk probability map, and it can remove grid-like artifacts by uniformly weighting method. Finally, the global classification network uses the global disease risk probability map for the early detection of AD. We evaluate the proposed method on the OASIS-3, AIBL and ADNI datasets, and experimental results show that the proposed RClaNet achieves superior registration performances than several state-of-the-art methods. Early diagnosis of AD using the global disease risk probability map also yielded competitive results. To demonstrate the generality, we also evaluate the proposed method on a COVID-19 dataset and achieve decent registration and classification results. These experiments prove that the deformation information in the registration process can be used to characterize subtle changes of degenerative diseases and further assist clinicians in diagnosis.