Glioma segmentation in multi-modal MRI is critical for clinical decision-making, but it remains challenging due to tumor heterogeneity and the complexity of multi-modal feature fusion. Although recent deep learning methods have achieved notable progress, they often struggle to effectively extract discriminative feature information, and achieve effective cross-modal fusion. To address these challenges, we propose an Anatomical Symmetry-guided Multi-modal Fusion Network (ASMFNet) for precise glioma segmentation, which explicitly leverages anatomical symmetry. First, a hierarchical multi-encoder-decoder architecture is proposed to capture multi-modal complementary information. Second, an anatomical symmetry guidance module is proposed to guide the network to exploit tumor-induced asymmetries. Third, an intra-modality purification module is proposed to suppress irrelevant features while enhancing glioma-related features. Furthermore, a cross-modal fusion module is proposed to enable adaptive and context-aware multi-modal fusion. Finally, an anatomically-informed segmentation loss function is designed based on the characteristics of glioma to boost model convergence and improve segmentation performance. Extensive ablation and comparative experiments conducted on the BraTS datasets demonstrate that ASMFNet achieves statistically significant improvements over state-of-the-art methods, demonstrating its potential for reliable and clinically applicable brain tumor segmentation.

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 Feature Rectification and adaptive Modality Fusion guided multi-modal brain tumor segmentation Network, 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 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.

Accurate brain tumor segmentation remains challenging due to tumor heterogeneity and the disruption of normal anatomical structures. To address these issues, we propose FL-AMNet, a novel federated learning-based multi-modal fusion network for brain tumor segmentation. Specifically, FL-AMNet employs modality-separated encoders to preserve distinct feature representations. In addition, an Anatomical Symmetry Guidance (ASG) module is introduced to explicitly capture tumor-induced asymmetries by contrasting hemispheric features, thereby guiding the model to focus on pathological regions. Furthermore, an anatomically adaptive weighted loss is designed to alleviate class imbalance among tumor subregions. To enhance clinical applicability, FL-AMNet is deployed within a federated learning framework, enabling privacy-preserving multi-center collaboration. Notably, its compact architecture reduces communication overhead, supporting efficient distributed training. Evaluations on the BraTS datasets demonstrate that FL-AMNet achieves state-of-the-art segmentation accuracy while maintaining a highly compact parameter count. These results validate the effectiveness of the symmetry-driven architectural design and highlight its strong potential for practical clinical deployment.

Accurate brain tumor segmentation from MRI scans is critical for effective diagnosis and treatment planning. Recent advances in deep learning have significantly improved brain tumor segmentation performance. However, these models still face challenges in clinical adoption due to their inherent uncertainties and potential for errors. In this paper, we propose a novel MR brain tumor segmentation approach that integrates multi-modal data fusion and uncertainty quantification to improve the accuracy and reliability of brain tumor segmentation. Recognizing that each MR modality contributes unique insights into the tumor’s characteristics, we propose a novel modality-aware guidance by explicitly categorizing the modalities into ”teacher” (FLAIR and T1c) and ”student” (T2 and T1) groups. Since the teacher modalities are the most informative modalities for identifying brain tumors, we propose a multi-modal teacher-student fusion strategy. This strategy leverages the teacher modalities to guide the student modalities in both spatial and channel feature representation aspects. To address prediction reliability, we employ Monte Carlo dropout during training to generate multiple uncertainty estimates. Additionally, we develop a novel uncertainty-aware loss function that optimizes segmentation accuracy while quantifying the uncertainty in predictions. Experimental results conducted on three BraTS datasets demonstrate the effectiveness of the proposed components and the superior performance compared to the state-of-the-art methods, highlighting their potential for clinical application.

Multi-view feature selection, aiming to identify discriminative features from heterogeneous views, has become a fundamental research area in multi-view learning. Previous methods mainly project the features from each view into the label space, ignoring the underlying interaction between different projection subspaces. Moreover, the l2,p-norm (0<p≤1) constraint is often imposed on the view-specific projections to make them sparse in rows, which not only involves an additional feature sorting process but also affects the stability and effectiveness of selected feature subsets. To this end, a novel Multi-view Feature Selection method with adaptive projection subspace Fusion (MFSF) is proposed to select features from the joint feature projection across all views. Specifically, MFSF fully uses the label guidance and view-specific projections to learn the joint projection subspace from multiple views, thereby leveraging the consistency and complementarity among views. Furthermore, the adaptive fusion and learning of multiple projection subspaces are embedded into the l2,0-norm feature selection, guaranteeing the automatic selection of stable and discriminative features and avoiding the subsequent feature sorting process. An optimization strategy with fast convergence is further developed to solve the objective function of MFSF, and extensive experiments demonstrate the effectiveness and superiority of MFSF against state-of-the-art competitors.

Convolutional neural networks (CNNs) have shown promising performance in the segmentation of lymphoma lesions. It offers a significant reduction in manual annotation efforts and mitigates discrepancies arising from varying levels of expertise. However, most existing methodologies focus primarily on the integration of positron emission tomography (PET) and computed tomography (CT) modalities, or on improving the representation of network features. This ranges from fine-tuning the encoder backbones to enhancing segmentation accuracy, all while paying little attention to the intricate nuances of lymphoma lesions. The challenge persists due to the Partial Volume Effect (PVE) and the dearth of spatial information, which pose difficulties in delineating clear edges. To address this problem, this paper proposes a multi-head fusion edge enhancement network architecture. This architecture incorporates a 3-branch encoding structure that captures edge, PET, and CT information separately, followed by their fusion via a streamlined feature fusion module. Furthermore, an auxiliary head supervised by edge information is integrated to refine the edges. Extensive experiments validate the superiority of the proposed architecture. Compared with the existing methods, our method has an absolute gap of 2.02 and 1.39 in Dice score, respectively, when compared with the U-Net-based model and the model not based on U-Net. Furthermore, it explicitly overcomes the tissue adhesion issues caused by the PVE and substantially suppresses false-positive predictions in adjacent healthy tissues, successfully delineating smoother boundaries and capturing finer morphological details of multi-scale lymphoma lesions.

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 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 brain tumor segmentation from multi-modal MRI is critical for clinical diagnosis and treatment planning. However, effectively exploiting the complementary information across different modalities remains challenging due to modality-specific noise, semantic inconsistency and inherent model uncertainty. To tackle these issues, we propose a Disentangled Fusion Network named DFuse-Net that integrates disentangled feature fusion with uncertainty-aware learning for reliable multi-modal brain tumor segmentation. Specifically, DFuse-Net explicitly disentangles modality-shared and modality-specific representations, enhancing the discriminability and expressiveness of multi-modal features. Furthermore, a Disentangled Texture Fusion Module (DTFM) and a Disentangled Semantic Fusion Module (DSFM) are designed to effectively integrate texture- and semantic-level information across modalities. In addition, a contrastive-aware learning scheme is proposed to strengthen feature discriminability, while a consistency-aware learning strategy is proposed to enforce structural coherence across modalities. During inference, Monte Carlo dropout is employed to estimate voxel-wise aleatoric and epistemic uncertainties, improving segmentation reliability. Extensive experiments on the BraTS datasets demonstrate that DFuse-Net outperforms state-of-the-art methods, suggesting its potential for reliable clinical application in brain tumor diagnosis and treatment planning.

In recent years, brain tumor segmentation has emerged as a critical area of focus in medical image analysis. Accurate tumor delineation is essential for effective treatment planning and patient monitoring. Many existing algorithms struggle with accurately delineating complex tumor boundaries, particularly in cases where tumors exhibit heterogeneous features or blend with surrounding healthy tissues. In this paper, I propose a novel boundary-aware multi-modal brain tumor segmentation network, which integrates four key contributions to improve segmentation accuracy. First, I introduce a Boundary Extraction Module (BEM) to capture essential boundary information for segmentation. Second, I present a Boundary Guidance Module (BGM) to guide the segmentation process by incorporating boundary-specific information. Third, I design a Boundary Supervision Module (BSM) to enhance segmentation accuracy by providing multi-level boundary supervision. Lastly, I propose a Cross-feature Fusion (CFF) that integrates complementary information from different MRI modalities to enhance overall segmentation performance. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, achieving superior tumor segmentation accuracy across brain tumor segmentation datasets, thereby indicating its potential for clinical applications in neuroimaging.

Brain tumor segmentation is crucial for accurate diagnosis, treatment planning, and patient monitoring. Different MRI sequences can provide unique and complementary information about various aspects of brain tumors. However, effectively integrating diverse data sources to achieve accurate segmentation remains a significant challenge due to the inherent complexity and variability of the data. To address this challenge, this paper proposes a deep learning framework designed to fuse multi-modal MRI data and enhance brain tumor segmentation accuracy. Specifically, the framework introduces two innovative modules: the modality-wise feature fusion module (MFFM) and the spatial and channel-wise feature fusion module (SCFFM). The MFFM aims to learn modality-specific features and integrate information from diverse modalities, thereby ensuring richer and more discriminative feature representations. Meanwhile, the SCFFM is designed to capture contextual information and achieve multi-channel data incorporation by emphasizing informative regions and highlighting critical features. Together, these modules collaboratively enhance the model's capacity for feature learning, leading to more precise tumor segmentation. Experimental validation on two public datasets demonstrates the effectiveness of the proposed approach, achieving an average Dice similarity coefficient of 83.2% with an average 95% Hausdorff distance of 4.3 mm on the BraTS 2018 dataset, and an average Dice similarity coefficient of 82.9% with an average 95% Hausdorff distance of 5.5 mm on the BraTS 2019 dataset. This framework not only presents an effective method for precise multi-modal brain tumor segmentation but also provides a promising solution for other challenges in multi-modal data fusion.

Dilated convolution is a powerful technique for expanding the receptive field without increasing the convolution kernel size, making it highly valuable in image segmentation tasks. However, some challenges still exist, such as sparse feature extraction at high dilation rates and limited neighboring information interaction. To address these issues, a novel approach, termed Dynamic Dilated Convolution (DDConv), is proposed for adaptive feature extraction by dynamically adjusting dilation rates in this paper. In DDConv, dilated convolutions are divided into four groups along the channel, each assigned distinct dilation rates. Attention weights are then used to aggregate information across these groups, enabling the convolution kernel to dynamically select the dilation rate during feature map convolution. Experimental results demonstrate the effectiveness of the proposed methods, with DDConv delivering outstanding performance. These findings suggest promising implications for DDNeXt as a novel backbone construction approach for future developments in the field.

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.

Brain tumor segmentation plays a critical role in the diagnosis and treatment planning of brain tumors. However, achieving accurate segmentation is challenging due to the complex boundaries between different tumor sub-regions. Additionally, many existing methods produce deterministic segmentation results without addressing prediction uncertainty, limiting their reliability and interpretability in clinical applications. To tackle these challenges, this paper proposes a novel Boundary-aware and Uncertainty-driven multi-modal Fusion Network (BUFNet) for MR brain tumor segmentation. Specifically, a boundary-aware mechanism is proposed to extract tumor boundary information, and guide the network by leveraging this information for better discrimination of tumor sub-regions. Furthermore, an effective multi-modal fusion method is proposed to integrate complementary information from multiple MR modalities. To further reduce uncertainty, a novel uncertainty-based segmentation loss function is proposed to improve segmentation performance. Additionally, to enhance clinical interpretation and decision-making, uncertainty quantification is incorporated to provide confidence measures for segmentation results. Experimental results demonstrate the effectiveness of the proposed method, showing superior performance compared to state-of-the-art methods.

Brain tumors are threatening the life and health of people in the world. Automatic brain tumor segmentation using multiple MR images is challenging in medical image analysis. It is known that accurate segmentation relies on effective feature learning. Existing methods address the multi-modal MR brain tumor segmentation by explicitly learning a shared feature representation. However, these methods fail to capture the relationship between MR modalities and the feature correlation between different target tumor regions. In this paper, I propose a multi-modal brain tumor segmentation network via disentangled representation learning and region-aware contrastive learning. Specifically, a feature fusion module is first designed to learn the valuable multi-modal feature representation. Subsequently, a novel disentangled representation learning is proposed to decouple the fused feature representation into multiple factors corresponding to the target tumor regions. Furthermore, contrastive learning is presented to help the network extract tumor region-related feature representations. Finally, the segmentation results are obtained using the segmentation decoders. Quantitative and qualitative experiments conducted on the public datasets, BraTS 2018 and BraTS 2019, justify the importance of the proposed strategies, and the proposed approach can achieve better performance than other state-of-the-art approaches. In addition, the proposed strategies can be extended to other deep neural networks.

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.

Accurate brain tumor segmentation is an essential step for clinical diagnosis and surgical treatment. Mul-timodal brain tumor segmentation strongly relies on an effective fusion method and an excellent seg-mentation network. However, it is common to have some missing MR modalities in clinical scenarios due to image corruption, acquisition protocol, scanner availability and scanning cost, which can heavily decrease the tumor segmentation accuracy, and also cause information loss for down-streaming disease analysis. To address this issue, I propose a novel multimodal feature fusion and latent feature learning guided deep neural network. On the one hand, the proposed network can help to segment brain tumors when one or more modalities are missing. On the other hand, it can retrieve the missing modalities to compensate for incomplete data. The proposed network consists of three key components. First, a Mul-timodal Feature Fusion Module (MFFM) is proposed to effectively fuse the complementary information from different modalities, consisting of a Cross-Modality Fusion Module (CMFM) and a Multi-Scale Fu-sion Module (MSFM). Second, a Spatial Consistency-based Latent Feature Learning Module (SC-LFLM) is presented to exploit multimodal latent correlation and extract the relevant features to benefit segmenta-tion. Third, the Multi-Task Learning (MTL) paths are integrated to supervise the segmentation and recover the missing modalities. The proposed method is evaluated on BraTS 2018 dataset, and it can achieve su-perior segmentation results when one or more modalities are missing, compared with the state-of-the-art methods. Furthermore, the proposed modules can be easily adapted to other multimodal network archi-tectures and research fields.(c) 2023 Elsevier Ltd. All rights reserved.

Multimodal MR brain tumor segmentation is one of the hottest issues in the community of medical image processing. However, acquiring the complete set of MR modalities is not always possible in clinical practice, due to the acquisition protocols, image corruption, scanner availability, scanning cost or allergies to certain contrast materials. The missing information can cause some restraints to brain tumor diagnosis, monitoring, treatment planning and prognosis. Thus, it is highly desirable to develop brain tumor segmentation methods to address the missing modalities problem. Based on the recent advancements, in this review, we provide a detailed analysis of the missing modality issue in MR-based brain tumor segmentation. First, we briefly introduce the biomedical background concerning brain tumor, MR imaging techniques, and the current challenges in brain tumor segmentation. Then, we provide a taxonomy of the state-of-the-art methods with five categories, namely, image synthesis-based method, latent feature space-based model, multi-source correlation-based method, knowledge distillation-based method, and domain adaptation-based method. In addition, the principles, architectures, benefits and limitations are elaborated in each method. Following that, the corresponding datasets and widely used evaluation metrics are described. Finally, we analyze the current challenges and provide a prospect for future development trends. This review aims to provide readers with a thorough knowledge of the recent contributions in the field of brain tumor segmentation with missing modalities and suggest potential future directions.

Brain tumor segmentation from Magnetic Resonance Imaging is essential for early diagnosis and treatment planning for brain cancers in clinical practice. However, existing brain tumor segmentation methods cannot sufficiently learn high-quality feature information for segmentation. To address this issue, a modality-level cross-connection and attentional feature fusion based deep neural network is proposed for multi-modal brain tumor segmentation. The proposed method can not only locate the whole tumor region but also can accurately segment the sub-tumor regions. The proposed network architecture is a multi-encoder based 3D U-Net. Inspired by the characteristics of multi-modalities, a modality-level cross-connection (MCC) is first proposed to take advantage of the complementary information between the related modalities. Moreover, to enhance the feature learning capacity of the network, the attentional feature fusion module (AFFM) is proposed to fuse the multi-modalities as well as to extract the useful feature representation for segmentation. It consists of two components: multi-scale spatial feature fusion (MSFF) block and dual-path channel feature fusion (DCFF) block. They aim at learning multi-scale spatial contextual information and the channel-wise feature information to improve the segmentation accuracy. Also, the proposed fusion module can be easily integrated into other fusion models and deep neural network architectures. Comprehensive experiments evaluated on the BraTS 2018 dataset demonstrate that the proposed network architecture can effectively improve the brain tumor segmentation performance when compared with the baseline methods and the state-of-the-art methods.