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Background: Manual segmentation of thymoma is an onerous, labor-intensive, and subjective task for radiologists. Accordingly, the development of an automatic and efficient method for thymoma segmentation can be valuable for the early detection and diagnosis of this malignancy. Methods: Three hundred and ten subjects were enrolled in this retrospective study and all underwent CECT scans. All the scans were manually labeled by four experienced radiologists. The successful application of convolution neural networks (CNNs) and Transformer in computer vision led us to propose a hybrid CNN–Transformer architecture, named transformer attention Net (TA-Net), that would allow the utilization of both local information from CNN features and the global information encoded by Transformers. U-Net was used as the basic structure and Transformers were inserted into convolution blocks in the encoder. In addition, attention gates were embedded in skip connections to highlight salient features. Comparison of the accuracy, intersection over Union (IoU), Dice score, and Boundary F1 contour matching score (BFScore) between the predicted segmentation and the manual labels were utilized to evaluate segmentation performance. Results: For thymoma segmentation using TA-Net, the accuracy, Dice score, IoU, and BFScore were 92.49%, 89.92%, 83.80%, and 0.8945, respectively, and no significant differences were detected among tumor types and enhanced phases. Our proposed method achieved the best performance when compared with state-of-the-art methods. Conclusion: The proposed method, which combines CNNs with Transformer, achives outstanding performance in thymoma segmentation compared with previous methods. TA-Net may provide consistent and reproducible delineation, thereby assisting radiologists in clinical applications.

Type 2 diabetes mellitus (T2DM) and sleep disorders (SD) have become important and costly health issues worldwide, particularly in China. Both are common diseases related to brain functional and structural abnormalities involving the hypothalamic-pituitary-adrenal (HPA) axis. The brains of individuals who suffer from both diseases simultaneously might be different compared to healthy individuals. This review assessed current neuroimaging findings to develop alternative targeted treatments for T2DM and SD. Relevant articles published between January 2002 and September 2021 were searched in PubMed and Web of Science databases. Generalized treatment methods for T2DM include dietary/weight-loss management, metformin or a combination of two non-insulin drugs, and melatonin for SD, though alternative therapies including electroacupuncture (EA) have been utilized in treating both of these diseases separately because they are convenient, affordable, and safe. Standard and alternative treatments for T2DM were somehow effective in treating SD. Neuroimaging studies of these disorders can achieve higher treatment efficacy by targeting brain areas, such as the hypothalamus (HYP), as visualized via diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI). DTI and fMRI can map the human brain and are utilized in many experiments. Thus, we propose that neuroimaging studies could be used in treatment of SD in T2DM.