2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
TL;DR
This study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data that enables precise tumor delineation and is suitable for large-scale clinical integration.
Abstract
Brain neoplasm is life-threatening abnormalities that require accurate segmentation for effective diagnosis along with treatment planning. Existing segmentation methods often depend on manual annotations or supervised learning, which are time-consuming, subjective, and not scalable. To overcome these challenges, this study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data. The 3D MRI volumes are first sliced into 2D axial views and preprocessed to enhance quality. K-means clustering is then applied to generate pseudo-labels that distinguish tumor from healthy regions. These labels guide M-Net CNN training process, which produces coarse tumor localization. A U-Net model further refines the segmentation by fusing the original sliced MRI inputs with M-Net CNN outputs to enhance boundary precision and anatomical alignment. The proposed pipeline achieved a high Dice coefficient of 84.2%, demonstrating its accuracy and robustness. This multi-stage, annotation-free approach enables precise tumor delineation and is suitable for large-scale clinical integration.
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex structure, irregular shape, intensity variation, and heterogeneous appearance of brain tumors make automated segmentation challenging. This study presents a comparative deep learning framework for brain tumor segmentation using the BraTS 2020 dataset and two-dimensional (2D) MRI images. In this study, we explore 2D deep learning architectures for automated tumor segmentation, focusing on U-Net, Vision Transformer (ViT), and Res-ViT models. U-Net, with its encoder–decoder design and skip connections, has been widely adopted for medical image segmentation due to its ability to capture fine-grained spatial features. ViT, leveraging self-attention mechanisms, introduces a global receptive field that enhances contextual understanding across slices. The Res-ViT hybrid combines residual learning with transformer-based attention, aiming to balance local feature extraction and long-range dependency modelling. Preprocessing steps, including skull stripping, intensity normalisation, and bias field correction, were applied to ensure consistency across scans. Data augmentation techniques such as rotation, flipping, and elastic deformation were employed to mitigate overfitting and improve generalisation. The models are evaluated using important segmentation metrics, including Intersection over Union (IoU), accuracy, precision, recall/sensitivity, loss, and 95th-percentile Hausdorff Distance (HD95). The comparative analysis aims to identify the strengths and limitations of convolutional and transformer-based approaches for 2D brain tumor segmentation. The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images.
Keywords: MRI; U-Net; ViT; BraTS 2020; IOU; HD95.
Lovedeep Kaur, Parminder Singh, Naveen Dhillon· International Journal of Com...· 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
Automatic brain Magnetic Resonance Imaging (MRI) analysis plays a crucial role in computer-aided diagnosis, treatment planning, and disease monitoring by enabling accurate delineation and identification of brain tumors. However, manual segmentation is labor-intensive, time-consuming, and susceptible to inter-observer variability, making automated and reliable methods highly desirable. This paper proposes a Hybrid U-Net++ and Vision Transformer (ViT) Fusion framework for automatic brain MRI tumor segmentation and segmentation-guided classification. The proposed architecture employs a U-Net++ encoder with nested skip connections to extract hierarchical multi-scale features, while a Vision Transformer captures long-range spatial dependencies and global contextual information through self-attention mechanisms. A multi-stage feature fusion strategy integrates convolutional and transformer representations, complemented by boundary-aware refinement to improve tumor localization and preserve fine anatomical details. The segmentation model achieved a training Dice score of 95.6%, IoU of 91.4%, and validation Dice score of 92.6%, demonstrating stable convergence and strong generalization capability. Furthermore, the proposed framework attained a Dice score of 95.8%, IoU of 92.1%, and Hausdorff Distance of 3.35 mm in the ablation analysis, confirming the effectiveness of multi-stage feature fusion and boundary-aware refinement. The segmented tumor regions are subsequently utilized for Region of Interest (ROI)-based classification using deep feature extraction, Global Average Pooling (GAP), and fully connected layers. The classification module distinguishes glioma, meningioma, pituitary tumor, and no-tumor cases, achieving an overall accuracy of 97.55%, macro-average precision of 97.13%, recall of 97.50%, F1-score of 97.31%, and specificity of 98.40%. Comparative analysis further demonstrates the effectiveness of the proposed hybrid architecture over conventional CNN-based and transformer-based approaches. The combined segmentation and classification results indicate that the proposed framework provides an effective approach for tumor localization and tumor-type identification, with potential applicability in automated brain MRI analysis and clinical decision support.
Malathi Janapati, Shaheda Akthar· International journal of re...· 0 citations
Segmentation of brain tumors in Magnetic Resonance Imaging (MRI) has many applications in the diagnosis, treatment planning, and monitoring of the disease. The manual delineation of tumor sub-regions is time-consuming and can be influenced by inter-observer variability, requiring automated solutions. This study introduces an AI-based 3D brain tumor segmentation system based on a 3D U-Net architecture to segment brain tumors based on multi-classes with multi-modal MRI volumetric data. Four MRI modalities are processed, and the tumor regions are divided into edema, necrotic core, and enhancing tumor in the proposed model. To address class imbalance and enhance segmentation accuracy, a loss function based on the Dice coefficient is used for training. Besides segmentation, the system consists of quantitative tissue analysis via calculation of the relative distribution of tumor sub-regions and interactive visualization using a web-based dashboard. The experiments demonstrated successful volumetric segmentation and significant tissue quantification. The proposed framework emphasizes a system-level integration of segmentation, quantification, and visualization to enhance interpretability and practical usability.
D. U. Latha, M. Padma, D. Rajeshwari et al.· Engineering, Technology &...· 0 citations
Brain tumors are malformed tissues which are present in the brain hindering the normal functions of nerves and can cause serious intellectual degradation or even death when untreated. The timely and accurate diagnosis of these tumors is essential in development of effective interventions, minimizing the mortality rate, and delivery of quality patient services. Of course, the manual segmentation of the tumors in the MRI results is time-consuming, subjective, and likely to cause inconsistency between clinicians. To solve these difficulties, the paper suggests a hybrid segmentation framework associating classical methods of image processing with the methods inspired by machine learning. In particular, gray-level thresholding (Otsu), morphological operations, distance map, and watershed segmentation are combined in order to increase the visibility and outlining of the tumor. Segments layer-by-layer based on the proposed method achieved 92.7 percent segmentation accuracy on the BRATS 2020 dataset, and both Dice, and Intersection over Union scores, matched those of complex deep learning models, without the resource requirements of strong computer systems or large-scale annotations. The method is best to be deployed in resource-limited medical settings because of these features. On balance, the framework offers a comprehensible, high-speed, and effective method to brain tumor detection that has the capability of assisting clinical routine plus subsequent real-time diagnostic implementations.
Laishram Trinity, Usham Sanjota Chanu, Yumnam Jayanta Singh et al.· International journal of com...· 0 citations
Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and disease monitoring. However, developing automated segmentation models that generalize across diverse tumor characteristics, imaging protocols, acquisition sites, and patient populations remains challenging. Variations in tumor morphology and imaging distributions can substantially degrade performance outside the training domain. Consequently, improving the robustness and generalization of deep learning-based segmentation models has become a key objective in medical image analysis. To improve segmentation robustness, we propose Multi-Stage Dynamic Prompt nnU-Net, a prompt-conditioned extension of nnU-Net. Three independent dynamic prompt modules are inserted into the deepest encoder stages. Each module contains a learnable bank of ten 256-dimensional prompt vectors and uses globally pooled encoder features to generate image-specific prompt representations. These representations are projected into feature-wise scaling $(\gamma)$ and shifting $(\beta)$ parameters that modulate encoder feature maps through Feature-wise Linear Modulation (FiLM), enabling adaptive feature conditioning at multiple semantic levels. Evaluation on the BraTS GOAT validation dataset demonstrated that the proposed Multi-Stage Dynamic Prompt nnU-Net outperformed the baseline nnU Net across the majority of evaluated metrics and tumor subregions. The proposed model achieved average lesion-wise Dice scores of 76.16% (ET), 80.04% (TC), and 86.42% (WT), compared with 74.38%, 78.14% and 84.01% for the baseline model. The results demonstrate that multi-stage dynamic prompt conditioning improves segmentation accuracy and boundary delineation for brain tumor segmentation.
Mohammad Mahdi Danesh Pajouh, Sara Saeedi· 0 citations
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