Brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) is crucial for computer-assisted diagnosis and treatment planning. However, this task remains highly challenging due to substantial image heterogeneity, modality-inherent variability, and severe class imbalance among tumor sub-regions. To address these issues, we propose FRMF-Net , a F eature R ectification and adaptive M odality F usion guided multi-modal brain tumor segmentation Net work, which consists of three key components: a Modality-Specific Feature Rectification (MSFR) module, an Adaptive Modality Fusion (AMF) module, and a Region-Adaptive Loss (RAL). Specifically, MSFR enhances modality-specific representations by jointly modeling shared and private information, thereby mitigating inter-modality noise and reducing feature discrepancies across modalities. Building on this, AMF performs voxel-wise adaptive fusion through modality-, channel-, and spatial-wise attention, enabling the network to dynamically emphasize the most informative features for accurate tumor delineation. In addition, RAL alleviates the class imbalance issue by adaptively reweighting the contribution of each tumor sub-region according to its spatial extent in each sample. Extensive experiments on the BraTS 2019 and BraTS 2020 datasets demonstrate that FRMF-Net consistently outperforms the state-of-the-art methods, achieving superior Dice score and lower Hausdorff distance, particularly in small and challenging tumor regions. These results confirm that FRMF-Net provides a robust and effective solution for multi-modal brain tumor segmentation.

Accurate brain tumor segmentation plays an important role in clinical diagnosis, treatment planning, and therapeutic response monitoring. Multi-modal MRI provides complementary structural and functional information, but existing methods remain limited by their inadequate exploitation of cross-modal complementarity and their inability to effectively handle modality-specific disparities and redundant information. To address these challenges, this paper proposes a novel hierarchical teacher-student learning framework with adaptive cross-modal fusion. MRI modalities are grouped into teacher modalities (Flair and T1c) and student modalities (T2 and T1) based on their intrinsic tumor-related characteristics. Central to this framework is the Modality Guidance Module (MGM), which consists of two key components designed to achieve multi-modal feature distillation. Within MGM, the Modality Enhancement Module (MEM) extracts highly discriminative features from teacher modalities. While the Modality Fusion Module (MFM) leverages these features to guide and refine the learning of student modalities. To further capture inter-modal dependencies, a Cross-Modal Fusion Module (CMFM) is introduced to adaptively integrate complementary information across all modalities. Extensive experiments on the BraTS 2018, 2019 and 2020 datasets demonstrate that the proposed method achieves superior performance compared with state-of-the-art approaches. Beyond brain tumor segmentation, the hierarchical teacher-student paradigm and adaptive fusion strategy also hold potential for broader multi-modal image analysis tasks.

Accurate segmentation of brain tumors from multi-modal MRI is crucial for diagnosis and treatment planning. However, challenges such as severe class imbalance, modality-specific feature heterogeneity, and predictive uncertainty hinder reliable performance. In this work, we propose UTriGate-Net, a novel uncertainty-aware multi-modal brain tumor segmentation framework. First, we design a Triaxial Context Encoding (TCE) block that extracts anisotropic spatial features by applying directional convolutions along the axial, coronal, and sagittal planes, thereby enhancing 3D contextual representation. Second, we introduce a Gated Modality Fusion (GMF) module, which adaptively integrates complementary information across modalities through modality-specific gating weights that suppress redundancy while retaining salient features. Finally, to improve segmentation reliability, we develop an Uncertainty-Regularized Weighted Loss (URWL) that combines dynamic class-specific weighting to mitigate class imbalance with an entropy-based uncertainty penalty to encourage well-calibrated predictions. Experiments on the BraTS 2019 and 2020 datasets demonstrate that UTriGate-Net achieves superior segmentation accuracy and robustness, particularly in challenging subregions. Overall, the proposed framework offers a promising solution for reliable and precise brain tumor delineation in clinical practice.

• IMH-Net boosts segmentation accuracy via salient-first modeling and hypergraph fusion • IA-Mamba prioritizes salient regions and preserves fine-grained local details • CSCEM boosts channel spatial complementarity and suppresses crossmodal noise • CHB capture high-order skip connection dependencies to recover details in decoding • Experiments show IMH-Net beats prior methods and stays SOTAcompetitive Precise multimodal tumor segmentation is essential for radiotherapy target contouring, surgical planning, and therapeutic efficacy evaluation. PET provides metabolic activity information, whereas CT offers detailed anatomical structures; their complementarity improves segmentation reliability in complex cases. However, existing sequence-modeling schemes are susceptible to order bias induced by a fixed scanning order, and cross-modal fusion and skip-connection interactions often remain at low-order, coarse-grained levels, making it difficult to jointly achieve salient-region–prioritized modeling, noise suppression, and high-order semantic coupling. To address this, we propose IMH-Net, an automatic multimodal tumor segmentation network based on importance-aware Mamba and hypergraph modeling. The proposed network includes three core components: (1) importance-aware Mamba (IA-Mamba), which estimates patch importance in the encoder stage and dynamically reshuffles the scan order to model salient regions first. (2) The Cross-modal Spatial Channel Enhancement Module (CSCEM) performs cross-modal collaborative enhancement in both channel and spatial dimensions at the bottleneck, emphasizing complementary semantics while suppressing redundant conflicts. (3) The Cross-modal Hypergraph Bridge (CHB) constructs intra- and inter-modality hyperedges at skip connections and leverages hypergraph convolution and hypergraph attention to enable stable high-order interactions and feature coupling. Comprehensive experiments on the public STS, Hecktor 2022, and ECPC datasets validate both the effectiveness of the proposed modules and their complementary synergy. IMH-Net achieves Dice scores of 81.82%, 80.86%, and 91.40% on STS, Hecktor 2022, and ECPC datasets, respectively, outperforming state-of-the-art (SOTA) multimodal segmentation methods in overall performance.

Accurate brain tumor segmentation from multimodal MRI is critical for clinical diagnosis and treatment planning. However, effectively leveraging the complementary information across different modalities remains a significant challenge due to modality-specific noise, information redundancy and inherent model uncertainty. To tackle these challenges, we propose a Disentangled Fusion Network (DFuse-Net) that integrates disentangled feature fusion with contrastive and consistency-aware learning to enable reliable multi-modal brain tumor segmentation. Our method first explicitly disentangles modality-shared and modality-specific feature representations. Then, a Disentangled Feature Fusion Module (DFFM) is proposed to effectively integrate modality-shared and modalityspecific feature representations. In addition, a contrastive-aware learning scheme is employed to enhance feature discriminability, while a consistency-aware learning strategy is applied to enforce structural coherence across modalities. Moreover, Monte Carlo dropout is applied during inference to generate voxelwise aleatoric and epistemic uncertainty maps, enhancing the robustness of segmentation. Extensive experiments on the BraTS datasets demonstrate that DFuse-Net achieves superior segmentation accuracy and reliability compared to the state-of-the-art methods.

Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge framework, named multi-modal graph convolution network (M2GCNet), to explore the relationships across different MR modalities, and address the challenge of brain tumor segmentation. The core of M2GCNet is the multi-modal graph convolution module (M2GCM), a pivotal component that represents MR modalities as graphs, with nodes corresponding to image pixels and edges capturing latent relationships between pixels. This graph-based representation enables the effective utilization of both local and global contextual information. Notably, M2GCM comprises two important modules: the spatial-wise graph convolution module (SGCM), adept at capturing extensive spatial dependencies among distinct regions within an image, and the channel-wise graph convolution module (CGCM), dedicated to modelling intricate contextual dependencies among different channels within the image. Additionally, acknowledging the intrinsic correlation present among different MR modalities, a multi-modal correlation loss function is introduced. This novel loss function aims to capture specific nonlinear relationships between correlated modality pairs, enhancing the model's ability to achieve accurate segmentation results. The experimental evaluation on two brain tumor datasets demonstrates the superiority of the proposed M2GCNet over other state-of-the-art segmentation methods. Furthermore, the proposed method paves the way for improved tumor diagnosis, multi-modal information fusion, and a deeper understanding of brain tumor pathology.

Brain tumor is one of the leading causes of cancer death. The high grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient’s survival time. In this paper, we present a deep learning based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on public dataset BraTS 2018. Then, the pre-trained encoder is transferred to our private dataset to extract the semantic features. Following that, a multimodal fusion module and a nonlinear correlation learning module are designed to extract the effective features. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are introduced to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence sites from limited dataset.

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to miss some imaging modalities in clinical practice. In this paper, we present a novel brain tumor segmentation algorithm with missing modalities. Since it exists a strong correlation between multi-modalities, a correlation model is proposed to specially represent the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modality. First, the individual representation produced by each encoder is used to estimate the modality independent parameter. Then, the correlation model transforms all the individual representations to the latent multi-source correlation representations. Finally, the correlation representations across modalities are fused via attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 and BraTS 2019 dataset, it outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.

In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multimodality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the linear correlation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features represented by an extended scale invariant local binary pattern and colour intensities to characterise pixel representations. Local texture features achieve good tolerance against illumination variations in rich texture regions but not so efficiently on uniform regions, so a photometric invariant colour measurement is proposed to overcome its limitation. Both quantitative and qualitative evaluations carried out on a well‐known change detection dataset are provided to demonstrate the effectiveness of the proposed algorithm.