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Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer

arXiv 2407.08470 ↗First posted 11 Jul 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

This research presents an enhanced approach for precise segmentation of brain tumor masses in magnetic resonance imaging (MRI) using an advanced 3D-UNet model combined with a Context Transformer (CoT). By architectural expansion CoT, the proposed model extends its architecture to a 3D format, integrates it smoothly with the base model to utilize the complex contextual information found in MRI scans, emphasizing how elements rely on each other across an extended spatial range. The proposed model synchronizes tumor mass characteristics from CoT, mutually reinforcing feature extraction, facilitating the precise capture of detailed tumor mass structures, including location, size, and boundaries. Several experimental results present the outstanding segmentation performance of the proposed method in comparison to current state-of-the-art approaches, achieving Dice score of 82.0%, 81.5%, 89.0% for Enhancing Tumor, Tumor Core and Whole Tumor, respectively, on BraTS2019.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 11 Jul 2024.

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References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  6. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  7. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  8. 2021Contextual Transformer Networks for Visual Recognitioncited by 90
  9. 2020KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentationcited by 32
  10. 2022A Transformer-based Generative Adversarial Network for Brain Tumor Segmentationcited by 4
  11. 2022Category Guided Attention Network for Brain Tumor Segmentation in MRIcited by 3

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