Accurate cross-modality cardiac image segmentation is essential for effectively diagnosing and treating heart disease. Different imaging modalities help to determine suitable pre-procedure planning. However, most methods face the difficulty of spatial-temporal confounding, where the anatomy element and modality element of cardiac images are intertwined across both spatial and temporal dimensions. It is derived from the imaging diversity and structure diversity of cardiac images. The spatial-temporal confounding hinders knowledge transfer between cardiac images on different modalities. In this paper, we propose a novel dynamic causal learning (DCL) to solve spatial-temporal confounding. The DCL explores multi-dimensional causal intervention to consider not only the causal relationship between images and labels, but also the causality in time dimension and space dimension. It integrates historical optimal interventions and facilitates the transfer of this knowledge across temporal contexts. In addition, the DCL utilizes the diffusion mechanism to further ensure that the extracted anatomy element remains causal invariant, improving model performance across multiple imaging modalities. Extensive experiments on cross-modality cardiac images (MR, CT, and US) demonstrate the effectiveness of the DCL (mean Dice = 0.951), outperforming other advanced segmentation methods. DCL is freely accessible at https://github.com/asdww0721ww/DCL.

Accurate segmentation of gliomas in multi-modal MRI is a critical prerequisite for clinical diagnosis and treatment. Deep learning has made significant progress in automated glioma segmentation. However, existing methods still face challenges in multi-scale feature extraction and multi-modal feature fusion. This paper proposes a Global-Local Collaboration and Task-driven Fusion Network (GLC- TFNet) for glioma segmentation in multi-modal MRIs. To enhance multi-scale feature extraction, a global-local collaboration encoder is proposed, which utilizes a dual-path mechanism to synergistically integrate global contextual information with local details. For multi-modal feature fusion, a task-driven fusion module is designed, which incorporates clinical diagnostic priors as constraints to optimize the contributions of multi-modal features in critical regions. Comprehensive experiments conducted on two publicly released datasets, BraTS2020 and BraTS2021, demon-strate that GLC- TFNet outperforms current state-of-the-art methods. Specifically, it achieves Dice scores of 94.82%,90.08%, and 88.09% for the whole tumor, tumor core, and enhancing tumor on the BraTS2020 dataset, and 92.88%, 90.61 %, and 87.06% on the BraTS2021 dataset, respectively.

Semi-supervised learning methods combine a small amount of labeled data with a large amount of unlabeled data to achieve high-precision segmentation while reducing the annotation cost. However, existing semi-supervised learning methods usually focus on data-level perturbations or improvements in network structures, ignoring the problem of insufficient information interaction between different branches and regions in complex texture tasks. In addition, in scenarios that require high-resolution processing (such as 3D medical images), traditional Transformer-based methods are not only computationally expensive but also prone to overfitting problems. Therefore, to address the above problems, we propose a Mamba based Consistency Constraints network (MCC-Net) for semi-supervised 3D medical image segmentation. Specifically, the model is organized by a shared encoder and three-branch decoder architecture. First, a consistency regularization constraint mechanism is introduced to combine the segmentation map of one decoder with the pseudo-labels of other decoders, thereby capturing more valuable features in high-uncertainty areas and generating stable, low-entropy predictions; second, a new consistency loss function is designed to construct constraints between the signed distance map (SDM) of the two auxiliary decoders and the segmentation map of the main decoder to enhance the learning ability of the target geometric structure. In addition, a Fusion Mamba(FM) Block is proposed to improve the model’s capabilities in deep semantic feature extraction and computational efficiency by modeling long-distance dependent features. Experimental results on public dataset show that, compared with six state-of-the-art semi-supervised segmentation methods, our method achieves Dice scores of 89.48%, 91.46% and 91.96%, respectively, when 10%, 20% and 30% of labeled data are used for training, significantly outperforming the other methods. The experimental results show that the model has strong advantages in both segmentation accuracy and utilization of unlabeled data.

The segmentation of glioma is crucial for early diagnosis, according to a World Health Organization (WHO) 2021 report. For glioma diagnosis, 3D multi-modal brain MRI/CT imaging has become an essential tool, offering detailed information. Nowadays, deep learning frameworks have been applied to various medical imaging problems, including brain glioma segmentation. Recently, foundation models like Segment Anything Model (SAM) have emerged as pivotal tools in computer vision tasks. These models are trained using large (real-world) datasets, offering a generalized understanding of visual data and semantic key features. Therefore, the effective utilization of foundation models in medical imaging is a significant area of current research. However, the differences in data distribution between multi-modal medical images and real-world images present challenges in directly applying foundation models to medical imaging. Additionally, utilizing multi-modal images to extract crucial information and its fusion poses further challenges. To address these issues, we propose a framework using foundation model and novel strategies for multi-modal fusion. Our fusion adapters effectively integrate the information from different modalities to enhance glioma segmentation in multi-modal MRI scans. Our method outperforms current state-of-the-art methods for accurate segmentation of the glioma using private and publicly available brain MRI datasets, proving the effectiveness of our approach across different datasets and imaging modalities.

Automatic nuclei segmentation and classification (NSC) is a fundamental prerequisite in digital pathology analysis as it enables the quantification of biomarkers and histopathological features for precision medicine. Nuclei appear to be small, however, global spatial distribution and brightness contrast, or color correlation between the nucleus and background, have been recognized as key rationales for accurate nuclei segmentation in actual clinical practice. Although recent great breakthroughs in medical image segmentation have been achieved by Transformer-based methods, the adaptability of segmenting and classifying nuclei from histopathological images is rarely investigated. Also, the severe overlap of nuclei and the large intra-class variability are common in clinical wild data. Prevailing methods based on polygonal representations or distance maps are limited by empirically designed post-processing strategies, resulting in ineffective segmentation of large irregular nuclei instances. To address these challenges, we propose a keypoint-guided tri-decoder Transformer (PointFormer) for NSC simultaneously. Specifically, the overall NSC task is decoupled to a multi-task learning problem, where a tri-decoder structure is employed for decoding nuclei instance, edges, and types, respectively. The nuclei detection and classification (NDC) subtask is reformulated as a semantic keypoint estimation problem. Meanwhile, introduces a novel attention-guiding strategy to capture strong inter-branch correlations and mitigate inconsistencies between multi-decoder predictions. Finally, a multi-local perception module is designed as the base building block of PointFormer to achieve local and global trade-offs and reduce model complexity. Comprehensive quantitative and qualitative experimental results on three datasets of different volumes have demonstrated the superiority of the proposed method over prevalent methods, especially for the PanNuke dataset with an achievement of 70.6% on bPQ.

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

In this article, glioma segmentation in the glioma grading computer-aided diagnosis (CAD) system requires manual delineation from radiologists, adding substantially to their workload. Although automatic segmentation is powerful, it cannot fully delegate power to artificial intelligence. We propose an AI-powered radiomics algorithm based on slice pooling (AI-RASP). AI-RASP generated compress images by compressing the gray value of each magnetic resonance imaging slice for radiologists to segment manually. In addition, AI-RASP integrated radiomics models to verify the glioma grading effect and the availability of compressed images. AI-RASP significantly reduce the time of manual segmentation. Results reported on multicenter datasets reveal that our architecture is better than the traditional manual segmentation while being over five times faster. The radiomics model with slice pooling mechanism achieves an area under the curve values of 0.86, 086, and 0.83 in the validation cohorts. Radiologists and patients can benefit from a CAD system integrated with AI-RASP.