mlsanityData to 14 Oct 2026

3D MRI brain tumor segmentation using autoencoder regularization

arXiv 1810.11654 ↗First posted 27 Oct 2018cs.CV q-bio.NCcited by 328

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Cited by 20 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.

Key facts. Cited by 328 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 44; 2022: 43; 2023: 49; 2024: 73; 2025: 68; 2026: 51.

328 papers.

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  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  2. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  3. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  4. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentationcited by 266
  5. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
  6. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  7. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  8. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 179
  9. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 111
  10. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  11. 2024Deep Multimodal Learning with Missing Modality: A Surveycited by 92
  12. 2021TransMed: Transformers Advance Multi-modal Medical Image Classificationcited by 86
38 more
  1. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 65
  2. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 64
  3. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  4. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  5. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  6. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentationcited by 54
  7. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  8. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  9. 2023The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiographycited by 50
  10. 2024VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledgecited by 50
  11. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  12. 2021ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalitiescited by 43
  13. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  14. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  15. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  16. 2022One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputationcited by 40
  17. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  18. 2024BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentationcited by 39
  19. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  20. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  21. 2022NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentationcited by 34
  22. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentationcited by 34
  23. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  24. 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 30
  25. 2021Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencodercited by 27
  26. 2023Semantic Image Segmentation: Two Decades of Researchcited by 27
  27. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  28. 2021Representation Disentanglement for Multi-modal brain MR Analysiscited by 22
  29. 2022Cross-Modality Deep Feature Learning for Brain Tumor Segmentationcited by 22
  30. 2023Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencodercited by 21
  31. 2021Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetcited by 19
  32. 2021BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Imagescited by 19
  33. 2022Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysiscited by 19
  34. 2023Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challengecited by 19
  35. 2024MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysiscited by 19
  36. 2022Medical Image Segmentation on MRI Images with Missing Modalities: A Reviewcited by 18
  37. 2022Learning from partially labeled data for multi-organ and tumor segmentationcited by 18
  38. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 18

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