mlsanityData to 14 Oct 2026

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

arXiv 2305.09011 ↗First posted 15 May 2023eess.IV cs.CVcited by 24

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Abstract (arXiv)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

7 of 27 references.

References · 7

  1. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  4. 2017Medical Image Analysis using Convolutional Neural Networks: A Reviewcited by 99
  5. 2020Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastcited by 45
  6. 2019DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesiscited by 36
  7. 2022Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approachcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 12; 2025: 5; 2026: 5.

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  1. 2024Similarity and Quality Metrics for MR Image-To-Image Translationcited by 20
  2. 2024cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesiscited by 19
  3. 2024Medical Vision Generalist: Unifying Medical Imaging Tasks in Contextcited by 13
  4. 20242.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correctioncited by 13
  5. 2024BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023cited by 11
  6. 2024BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectivescited by 7
  7. 2024Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imagingcited by 7
  8. 2023MRI Scan Synthesis Methods based on Clustering and Pix2Pixcited by 6
  9. 2024Five Pitfalls When Assessing Synthetic Medical Images with Reference Metricscited by 4
  10. 2024Brain Tumour Removing and Missing Modality Generation using 3D WDMcited by 4
  11. 2024Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesiscited by 4
  12. 2023CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translationcited by 3
12 more
  1. 2024CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesiscited by 3
  2. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 3
  3. 2026Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matchingcited by 2
  4. 2024Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentationcited by 1
  5. 2025Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentationcited by 1
  6. 2025Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesiscited by 0
  7. 2025DEMIST: Decoupled Multi-stream latent diffusion for Quantitative Myelin Map Synthesiscited by 0
  8. 2025Not Quite Anything: Overcoming SAMs Limitations for 3D Medical Imagingcited by 0
  9. 2026Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Trainingcited by 0
  10. 2026MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentationcited by 0
  11. 2026Seeing What Is Actually There: PriVE-Bench and PriVE-Tools for Counterfactual Evaluation of Agentic Visual Evidence in VLMscited by 0
  12. 2026MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Modelscited by 0