Experimental results demonstrate that the proposed 3D U-Net achieves superior performance, with a Dice coefficient of 0.83 and a Jaccard index of 0.82, outperforming baseline models across all evaluation metrics, indicating strong generalization capability.
Abstract
Medical imaging plays a crucial role in the accurate detection and localization of brain tumors, which is essential for effective clinical diagnosis and treatment planning. However, conventional segmentation approaches often struggle to capture complex spatial dependencies in volumetric data. To address this limitation, this study proposes an enhanced 3D U-Net architecture for multi-modal MRI-based brain tumor segmentation. The proposed model leverages three-dimensional convolutional operations to effectively capture contextual and spatial information from volumetric inputs. Additionally, an automated preprocessing pipeline, including image resizing, intensity normalization, and data augmentation, is incorporated to improve model robustness and generalization. The performance of the proposed model is evaluated against a conventional U-Net and a ResNet-based segmentation model using standard metrics such as Dice coefficient, accuracy, Intersection-over-Union (IoU), precision, recall, and F1-score. Experimental results demonstrate that the proposed 3D U-Net achieves superior performance, with a Dice coefficient of 0.83 and a Jaccard index of 0.82, outperforming baseline models across all evaluation metrics. Furthermore, the model exhibits improved convergence behavior and reduced overfitting, indicating strong generalization capability. These findings highlight the effectiveness of the proposed approach for volumetric medical image segmentation. Future work will focus on optimizing hyperparameters, enhancing architectural design, and validating the model on larger and more diverse clinical datasets.
Manual delineation is time-consuming, and inter-reader variability is high, making accurate delineation of glioma subregions in multimodal magnetic resonance imaging (MRI) important for treatment planning and longitudinal assessment. Current automatic techniques have limitations in identifying small enhancing regions, maintaining irregular tumor boundaries, and maintaining performance in the event of changes in image quality or input availability. This study proposes a 3D U-Net-based segmentation framework, NeuroFocusNet, which synchronizes spatial attention, channel recalibration, and attention-gated skip connections within a single volumetric encoder-decoder model. The framework also features intensity preprocessing using standardization, tumor-biased patch sampling, and a composite loss function comprising Dice, cross-entropy, focal, and boundary-aware loss terms. On the BraTS 2023 dataset, NeuroFocusNet achieved Dice scores across the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions of 0.9268, 0.9081, and 0.8707, respectively, with corresponding mean Hausdorff-95 distances of 1.68 mm, 1.36 mm, and 2.10 mm, respectively. A performance reduction was demonstrated when adding simulated Gaussian noise to the input, and was further demonstrated when a modality mask was applied during inference. Furthermore, 27 randomly selected cases were submitted to 3 different neuroradiologists for a preliminary evaluation of anatomical plausibility and correction requirements. This reader study is not meant to be a prospective or multicentre clinical validation of the framework. The results are encouraging when considering the feasibility of using NeuroFocusNet as a technically promising segmentation framework, and the need for externally controlled benchmarking, multicentre assessment, and prospective clinical assessment.
Faizan Ullah, Z. Abbas, Sergo Gegechkori et al.· IEEE Access· 0 citations
The problem of accurate identification of brain tumors using multi-modal MRIs still poses significant challenges due to tumor heterogeneity and variations across different image modes, as well as inconsistent availability of imaging modalities in real-world applications. Although deep learning algorithms such as convolution and transformer networks have demonstrated high efficacy in tumor segmentation through effective modeling of local and global contextual information, many state-of-the-art models perform directly on concatenated or fused images, making it difficult to leverage differences in information provided by different modalities and leading to inferior performance in cases of modality imbalance or missing data. To tackle the issue, we present in this paper a novel multimodal transformer network using the concept of reliability-driven modality attention for robust brain tumor segmentation. Our approach employs a feature extraction pipeline with a reliability estimator that automatically calculates weighting coefficients for each input modality (T1, T2, FLAIR, T1-CE), enabling more efficient feature representation than traditional fusion techniquesFurthermore, a slice-aware 2.5D context modeling strategy is used to capture inter-slice dependencies while keeping computational efficiency high compared to full 3D models. Extensive experiments on benchmark multi-modal MRI datasets show that the proposed approach achieves better segmentation performance than leading CNN, transformer, and hybrid methods, especially in scenarios with missing or degraded modalities. The results emphasize how reliability-aware fusion improves robustness, generalization, and clinical use of automated brain tumor analysis systems.
C. Nirmala, T. R. Ganesh Babu· International Conference on...· 0 citations
Brain tumor segmentation from MRI is clinically critical yet challenging due to heterogeneous appearance and irregular boundaries. Conventional CNN based methods lack effective global context modeling, while transformer-based approaches are computationally expensive and unstable on limited datasets. To address these, we propose MMA-UNet, a novel hybrid architecture that integrates multiple complementary mechanisms within an encoder-decoder framework. The model employs an EfficientNet-B5 encoder, MedNeXt bridge blocks, a Mamba-based VSS bottleneck, a CBAM enhanced decoder, and deformable refinement for precise boundary adaptation. The model achieves a Dice score of 0.9063, IoU of 0.8304, precision of 0.9043, recall of 0.9092, and specificity of 0.9982 on FigShare benchmark, outperforming the evaluated U-Net, Attention U-Net, TransUNet and Swin UNet under the adopted experimental protocol and maintaining parameter efficiency (30.41 M), demonstrating strong robustness on T1-weighted contrast-enhanced MRI. The proposed model operates on independent 2D slices without volumetric context, and its generalizability to larger datasets remains to be established.
Background Accurate segmentation of glioma subregions from multimodal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and response assessment, but remains challenging because of boundary ambiguity, heterogeneous appearance, and small enhancing tumor (ET) components. This study aimed to develop and evaluate a controlled nnU-Net v2-based framework for three-dimensional (3D) brain tumor segmentation (BraTS) by improving adaptive feature representation and boundary-aware learning while preserving the reproducibility of the self-configuring nnU-Net pipeline. Methods We propose a conditional convolution and squeeze-and-excitation with boundary-aware learning network (CondSEB-Net), a controlled enhancement of the nnU-Net v2 framework for 3D medical image segmentation. Conditional convolution (CondConv) is introduced to improve sample-specific feature adaptation, multi-level 3D squeeze-and-excitation (SE) attention is used to recalibrate channel responses, and a boundary-aware loss based on signed distance maps (SDMs) is incorporated to strengthen contour-level supervision. The proposed method was evaluated on BraTS2020 and kidney tumor segmentation (KiTS)2019 using fixed five-fold cross-validation and compared with representative 3D segmentation baselines. Results On BraTS2020, CondSEB-Net improved the average Dice score from 0.8525±0.0217 to 0.8659±0.0184 and reduced the average 95% Hausdorff distance (HD95) from 10.57±4.01 to 9.08±3.64 mm compared with nnU-Net v2. The average symmetric surface distance (ASSD) also decreased from 3.08±0.37 to 2.65±0.30 mm. Fold-wise paired statistical analysis showed statistically supported improvements in both region overlap and boundary-related metrics. On KiTS2019, CondSEB-Net also achieved superior or competitive performance with limited additional computational overhead. Conclusions CondSEB-Net improves adaptive feature representation and boundary-related segmentation performance while preserving the self-configuring pipeline of nnU-Net v2. The current results provide benchmark-level evidence for the proposed framework, while external multi-center validation remains necessary before clinical deployment.
Cheng-Hong Zhang, Qiang Wei· Quantitative Imaging in Medi...· 0 citations
CERD3D-UNet is introduced, a context-enhanced residual-dense 3D U-Net model with dual-attention mechanisms and hyperparameter optimization for accurate delineation of whole tumor, tumor core (TC), and enhanced tumor (ET).
Computer-aided diagnostic technologies have demonstrated substantial advantages in 3D medical image segmentation, particularly in multimodal 3D medical image segmentation tasks, where they play a pivotal role in driving continuous innovation in related architectures. As an integration of U-Net and Transformer, the UNETR architecture has demonstrated remarkable efficacy in 3D medical image segmentation. Nevertheless, despite its successes, UNETR remains challenged by clinical complexities such as intricate tumor localization and anatomical structural diversity in complex clinical settings. To address these issues, we propose an enhanced 3D segmentation framework, UAtten-Unetr, designed to improve segmentation accuracy and robustness in complex medical scenarios. The framework captures global contextual information via hierarchical Transformer layers and incorporates a spatial–channel attention module to enable adaptive fusion of multimodal features, thereby effectively enhancing cross-modal feature alignment capabilities. Concurrently, we innovatively developed a unified loss function based on bimodal modality-specific Dice constraints and uncertainty regularization, optimized for synchronous learning across the ACDC (cardiac MRI) and AMOS22 (abdominal CT/MRI) datasets. Experimental results showed that UAtten-Unetr achieved an average Dice score of 92.20% on the ACDC dataset, exceeding the reported nnU-Net result of 91.61% by 0.59 percentage points. On the AMOS22 dataset, the proposed method achieved an average Dice score of 84.51%, exceeding the reported UNETR result of 78.33% by 6.18 percentage points. However, its myocardium Dice score (84.11%) was lower than those of nnU-Net (89.24%) and MT-UNet (89.04%), indicating a remaining limitation in myocardium boundary segmentation. These results indicate competitive segmentation performance under the reported experimental settings. This method delivers dual improvements in accuracy and generalization across complex anatomical scenarios, providing an effective solution for precise diagnosis in intricate clinical environments.
Mengxuan Li, Hao-Yu Wang· Symmetry· 0 citations
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