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<p>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.</p>

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A 3D residual U-Net with attention-driven spatial pyramid pooling for accurate multimodal MRI brain tumor segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Retinderdeep Singh,
  • Chander Prabha,
  • Navita Gupta,
  • Durgesh Nandan,
  • Chandan Kumar Choubey

摘要

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.