Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention

• URL: http://arxiv.org/abs/2506.15562v2
• Date: Wed, 30 Jul 2025 09:53:31 GMT
• Title: Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
• Authors: Syed Haider Ali, Asrar Ahmad, Muhammad Ali, Asifullah Khan, Nadeem Shaukat,
• Abstract summary: Cancer is an abnormal growth with potential to invade locally and metastasize to distant organs.<n>Recent AI-based segmentation models are generally trained on large public datasets.<n>This study develops and integrates AI tumor segmentation models directly into hospital software for efficient and accurate oncology treatment planning and execution.
• Score: 0.7456526005219317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer is an abnormal growth with potential to invade locally and metastasize to distant organs. Accurate auto-segmentation of the tumor and surrounding normal tissues is required for radiotherapy treatment plan optimization. Recent AI-based segmentation models are generally trained on large public datasets, which lack the heterogeneity of local patient populations. While these studies advance AI-based medical image segmentation, research on local datasets is necessary to develop and integrate AI tumor segmentation models directly into hospital software for efficient and accurate oncology treatment planning and execution. This study enhances tumor segmentation using computationally efficient hybrid UNet-Transformer models on magnetic resonance imaging (MRI) datasets acquired from a local hospital under strict privacy protection. We developed a robust data pipeline for seamless DICOM extraction and preprocessing, followed by extensive image augmentation to ensure model generalization across diverse clinical settings, resulting in a total dataset of 6080 images for training. Our novel architecture integrates UNet-based convolutional neural networks with a transformer bottleneck and complementary attention modules, including efficient attention, Squeeze-and-Excitation (SE) blocks, Convolutional Block Attention Module (CBAM), and ResNeXt blocks. To accelerate convergence and reduce computational demands, we used a maximum batch size of 8 and initialized the encoder with pretrained ImageNet weights, training the model on dual NVIDIA T4 GPUs via checkpointing to overcome Kaggle's runtime limits. Quantitative evaluation on the local MRI dataset yielded a Dice similarity coefficient of 0.764 and an Intersection over Union (IoU) of 0.736, demonstrating competitive performance despite limited data and underscoring the importance of site-specific model development for clinical deployment.

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