Brain tumor image segmentation plays a significant auxiliary role in clinical diagnosis. Recently, deep learning has been introduced into multimodal segmentation tasks, which construct various Convolutional Neural Network (CNN) structures to achieve excellent performance. However, most CNN-based segmentation methods have poor capability for global feature extraction. Transformer is good at modeling long-distance dependencies, but it can cause local information loss and usually has a high computational complexity. In addition, it is difficult to fully exploit the brain tumor features of different modalities. To address these issues, in this paper, we propose a region–attention fusion (RAF) network that combines a dual-branch vision Transformer (DVT), called RFTNet. In RFTNet, the DVT is exploited to capture the delicate local information and global semantics separately by two branches. Meanwhile, a novel RAF is employed to effectively fuse the images of the different modalities. Finally, we design a new hybrid loss function, called region-mixed loss function (RML) to calculate the importance of each pixel and solve the problem of class imbalance. The experiments on BrasTS2018 and BraTS2020 datasets show that our method obtains a higher segmentation accuracy than other models. Furthermore, ablation experiments prove the effectiveness of each key component in RFTNet.

Medical image registration is a fundamental and indispensable element in medical image analysis, which can establish spatial consistency among corresponding anatomical structures across various medical images. Since images with different modalities exhibit different features, it remains a challenge to find their exact correspondence. Most of the current methods based on image-to-image translation cannot fully leverage the available information, which will affect the subsequent registration performance. To solve the problem, we develop an unsupervised multimodal image registration method named DTR-GAN. Firstly, we design a multimodal registration framework via a bidirectional translation network to transform the multimodal image registration into a unimodal registration, which can effectively use the complementary information of different modalities. Then, to enhance the quality of the transformed images in the translation network, we design a multiscale encoder–decoder network that effectively captures both local and global features in images. Finally, we propose a mixed similarity loss to encourage the warped image to be closer to the target image in deep features. We extensively evaluate methods for MRI-CT image registration tasks of the abdominal cavity with advanced unsupervised multimodal image registration approaches. The results indicate that DTR-GAN obtains a competitive performance compared to other methods in MRI-CT registration. Compared with DFR, DTR-GAN has not only obtained performance improvements of 2.35% and 2.08% in the dice similarity coefficient (DSC) of MRI-CT registration and CT-MRI registration on the Learn2Reg dataset but has also decreased the average symmetric surface distance (ASD) by 0.33 mm and 0.12 mm on the Learn2Reg dataset.

Accurate segmentation of nuclei is essential for structured analysis in digital pathology, directly influencing cancer diagnosis, grading, and prognosis. However, obtaining high-quality pixel-level annotations remains labor-intensive and time-consuming. Existing self-supervised learning methods primarily rely on low-level appearance representations to construct proxy tasks, which leads to limited sensitivity to nuclei morphology, suboptimal boundary discrimination, and insufficient modeling of spatial continuity. To mitigate these problems, a Self-Supervised Nuclei Segmentation Network with Saliency Guidance and Contextual Boundary Attention (SCB-Net) is proposed for instance segmentation of H&E stained images. First, SCB-Net incorporates the nuclei saliency guided self-supervision mechanism to generate structure-aware pseudo labels, which leverages cytological priors as structural driving signals to extract salient features by enhancing edge gradients and the structural continuity of nuclei. Then, during the segmentation stage, to enhance the structural expressiveness of the model in complex nuclei clusters and ambiguous boundary regions, the Contextual Boundary Attention (CBA) mechanism is designed to strengthen boundary directionality, reinforce edge gradients, and optimize cross-channel information fusion. Furthermore, the Efficient Triple Fusion (ETF) module is employed to concurrently fuse three complementary feature types (semantic, geometric structural, and visual boundary) to achieve cross-scale semantic alignment and geometrically consistent representations. Evaluations on the MoNuSeg, CPM17, and CryoNuSeg datasets demonstrate that SCB-Net attains AJI scores of 0.5609, 0.5102, and 0.4740, respectively. These results are comparable to weakly supervised methods despite requiring no ground-truth labels, and significantly surpass existing self-supervised approaches.