Papers › TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

7 Mar 2021arXiv:2103.04430archive 2025-07-28

Wenxuan Wang, Chen Chen, Meng Ding, Jiangyun Li, Hong Yu, Sen Zha

Transformer, which can benefit from global (long-range) information modeling using self-attention mechanisms, has been successful in natural language processing and 2D image classification recently. However, both local and global features are crucial for dense prediction tasks, especially for 3D medical image segmentation. In this paper, we for the first time exploit Transformer in 3D CNN for MRI Brain Tumor Segmentation and propose a novel network named TransBTS based on the encoder-decoder structure. To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps. Meanwhile, the feature maps are reformed elaborately for tokens that are fed into Transformer for global feature modeling. The decoder leverages the features embedded by Transformer and performs progressive upsampling to predict the detailed segmentation map. Extensive experimental results on both BraTS 2019 and 2020 datasets show that TransBTS achieves comparable or higher results than previous state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans. The source code is available at https://github.com/Wenxuan-1119/TransBTS

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Code

Wenxuan-1119/TransBTS officialmentioned in papermentioned on GitHubpytorch report

rubics-xuan/transbts mentioned on GitHubpytorch report

wenhui0206/Robust-TransBTS mentioned on GitHubpytorch report

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Tasks

Brain Tumor Segmentation Decoder Image Classification Image Segmentation Medical Image Segmentation Segmentation Semantic Segmentation Tumor Segmentation image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Brain Tumor Segmentation	BRATS 2019	Residual 3D U-Net ET	Dice Score	0.7163	#4 of 5	Archive leaderboard	report
Brain Tumor Segmentation	BRATS 2019	3D U-Net [6] ET	Dice Score	0.6876	#5 of 5	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

3D CNN Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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