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SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer.
Yun Jiang1, Yuan Zhang1, Xin Lin1
1College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Brain Sciences
|June 24, 2022
Summary
This study introduces SwinBTS, a novel approach for 3D brain tumor segmentation using a hybrid transformer and convolutional neural network model. SwinBTS enhances the accuracy of segmenting brain tumors in 3D MRI scans.
Area of Science:
- Medical Image Analysis
- Artificial Intelligence in Medicine
- Neuro-oncology Imaging
Background:
- Brain tumor semantic segmentation is crucial for diagnosis and treatment planning.
- Convolutional Neural Networks (CNNs) excel in computer vision but struggle with global context in 3D medical images.
- Transformers offer global information modeling but require integration with CNNs for medical imaging tasks.
Purpose of the Study:
- To develop an advanced 3D brain tumor segmentation method.
- To leverage the strengths of both transformers and CNNs for improved segmentation accuracy.
- To address the limitations of existing methods in capturing both local and global features.
Main Methods:
- Proposed SwinBTS, a hybrid model combining 3D Swin Transformer and CNNs.
- Utilized an encoder-decoder structure with the Swin Transformer for context extraction.
- Employed convolutional operations for efficient downsampling and upsampling.
- Integrated an improved Transformer module for detailed feature extraction.
Main Results:
- SwinBTS demonstrated superior performance in 3D brain tumor segmentation.
- Achieved state-of-the-art results on BraTS 2019, BraTS 2020, and BraTS 2021 datasets.
- Outperformed existing 3D algorithms for brain tumor segmentation on MRI scans.
Conclusions:
- SwinBTS effectively integrates transformer and CNN architectures for 3D medical image segmentation.
- The proposed method significantly improves the accuracy of brain tumor segmentation.
- SwinBTS represents a promising advancement in automated analysis of brain tumor imaging.
