mlsanityData to 7 Oct 2026

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer

arXiv 2103.04430 ↗First posted 7 Mar 2021cs.CV cs.AIcited by 265

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Abstract (arXiv)

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

Key facts. Cited by 265 papers in the map (data through 7 Oct 2026). Builds on 21 of its 24 references that are in the map. First posted on arXiv 7 Mar 2021.

Builds on

21 of 24 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020End-to-End Object Detection with Transformerscited by 7,384
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  8. 2017Non-local Neural Networkscited by 2,290
  9. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  10. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  11. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  12. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
9 more
  1. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  2. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  3. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  4. 2017Road Extraction by Deep Residual U-Netcited by 333
  5. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  6. 2020KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentationcited by 32
  7. 2020Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIscited by 12
  8. 2019Multi-step Cascaded Networks for Brain Tumor Segmentationcited by 11
  9. 2019Memory efficient brain tumor segmentation using an autoencoder-regularized U-Netcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 58; 2023: 62; 2024: 63; 2025: 40; 2026: 31.

265 papers.

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  1. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  2. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  3. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  4. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  5. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentationcited by 266
  6. 2022Transformers in Medical Imaging: A Surveycited by 245
  7. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  8. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  9. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  10. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  11. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
  12. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 179
38 more
  1. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
  2. 2024Medical SAM 2: Segment medical images as video via Segment Anything Model 2cited by 151
  3. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  4. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  5. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  6. 2021MISSFormer: An Effective Medical Image Segmentation Transformercited by 103
  7. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  8. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 94
  9. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  10. 20233DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationcited by 70
  11. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  12. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  13. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  14. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 42
  15. 2021A Robust Volumetric Transformer for Accurate 3D Tumor Segmentationcited by 41
  16. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  17. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  18. 2024BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentationcited by 36
  19. 2022D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentationcited by 35
  20. 2022Class-Aware Adversarial Transformers for Medical Image Segmentationcited by 33
  21. 2022NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentationcited by 33
  22. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  23. 2024SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentationcited by 31
  24. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  25. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  26. 2022HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classificationcited by 30
  27. 2023Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentationcited by 30
  28. 2022CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Networkcited by 29
  29. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  30. 2022CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentationcited by 24
  31. 2023Medical Vision Language Pretraining: A surveycited by 24
  32. 2024ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentationcited by 24
  33. 2022A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completioncited by 23
  34. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  35. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 22
  36. 20223D Vision with Transformers: A Surveycited by 21
  37. 2021BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentationcited by 19
  38. 2022Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoisingcited by 19

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