mlsanityData to 14 Oct 2026

SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

arXiv 2204.02961 ↗First posted 6 Apr 2022cs.CVcited by 31

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Abstract (arXiv)

Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the majority of brain tumours could well be identified exclusively by the use of Magnetic Resonance Imaging (MRI). Each MRI modality delivers distinct information on the soft tissue of the human brain and integrating all of them would provide comprehensive data for the accurate segmentation of the glioma, which is crucial for the patient's prognosis, diagnosis, and determining the best follow-up treatment. Unfortunately, MRI is prone to artifacts for a variety of reasons, which might result in missing one or more MRI modalities. Various strategies have been proposed over the years to synthesize the missing modality or compensate for the influence it has on automated segmentation models. However, these methods usually fail to model the underlying missing information. In this paper, we propose a style matching U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training approach utilizes a content and style-matching mechanism to distill the informative features from the full-modality network into a missing modality network. To do so, we encode both full-modality and missing-modality data into a latent space, then we decompose the representation space into a style and content representation. Our style matching module adaptively recalibrates the representation space by learning a matching function to transfer the informative and textural features from a full-modality path into a missing-modality path. Moreover, by modelling the mutual information, our content module surpasses the less informative features and re-calibrates the representation space based on discriminative semantic features. The evaluation process on the BraTS 2018 dataset shows a significant results.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 6 Apr 2022.

Builds on

23 of 34 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  4. 2015A Neural Algorithm of Artistic Stylecited by 483
  5. 2018Deep Co-Training for Semi-Supervised Image Recognitioncited by 147
  6. 2016HeMIS: Hetero-Modal Image Segmentationcited by 103
  7. 2019Bi-Directional ConvLSTM U-Net with Densley Connected Convolutionscited by 82
  8. 2019Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentationcited by 68
  9. 2021ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalitiescited by 43
  10. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  11. 2020On the Texture Bias for Few-Shot CNN Segmentationcited by 38
  12. 2020Semi-supervised few-shot learning for medical image segmentationcited by 34
11 more
  1. 2022Contextual Attention Network: Transformer Meets U-Netcited by 33
  2. 2020Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentationcited by 23
  3. 2019Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Datacited by 18
  4. 2022Medical Image Segmentation on MRI Images with Missing Modalities: A Reviewcited by 18
  5. 2021Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Imagescited by 11
  6. 2021MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networkscited by 10
  7. 2021Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentationcited by 10
  8. 2018Style Separation and Synthesis via Generative Adversarial Networkscited by 5
  9. 2021HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Imagescited by 5
  10. 2020Cross-Modal Information Maximization for Medical Imaging: CMIMcited by 3
  11. 2021Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalitiescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 7; 2024: 9; 2025: 6; 2026: 5.

31 papers.

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  1. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  2. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 95
  3. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  4. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  5. 2022TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentationcited by 43
  6. 2022TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Modelcited by 34
  7. 2023Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentationcited by 24
  8. 2024PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Ratescited by 17
  9. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  10. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  11. 2024Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentationcited by 8
  12. 2024Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Datacited by 5
19 more
  1. 2025DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalitiescited by 5
  2. 2025Learning A Robust RGB-Thermal Detector for Extreme Modality Imbalancecited by 5
  3. 2024Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalizationcited by 4
  4. 2024MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignmentcited by 4
  5. 2023CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translationcited by 3
  6. 2025Multimodal Masked Autoencoder Pre-training for 3D MRI-Based Brain Tumor Analysis with Missing Modalitiescited by 3
  7. 2024A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalitiescited by 1
  8. 2024Robust Divergence Learning for Missing-Modality Segmentationcited by 1
  9. 2025Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Trainingcited by 1
  10. 2025CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalitiescited by 1
  11. 2023Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentationcited by 0
  12. 2023Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentationcited by 0
  13. 2024Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learningcited by 0
  14. 2025Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using H"older Divergence and Mutual Information-Enhanced Knowledge Transfercited by 0
  15. 2026AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalitiescited by 0
  16. 2026Multicentric thrombus segmentation using an attention-based recurrent network with gradual modality dropoutcited by 0
  17. 2026CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalitiescited by 0
  18. 2026Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalitiescited by 0
  19. 2026Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalitiescited by 0