# The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) arXiv: https://arxiv.org/abs/2305.09011 ยท HTML: https://mlsanity.com/paper/2305.09011/ ## 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 (most cited first, 7 of 7) - [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021) - [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018) - [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016) - [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017) - [Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast](https://mlsanity.com/paper/2012.13340/) (2020) - [DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis](https://mlsanity.com/paper/1904.12894/) (2019) - [Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach](https://mlsanity.com/paper/2207.00844/) (2022) ## Built on by (most cited first, 24 of 24) - [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024) - [cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis](https://mlsanity.com/paper/2411.17203/) (2024) - [Medical Vision Generalist: Unifying Medical Imaging Tasks in Context](https://mlsanity.com/paper/2406.05565/) (2024) - [2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction](https://mlsanity.com/paper/2406.08374/) (2024) - [BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023](https://mlsanity.com/paper/2407.08855/) (2024) - [BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives](https://mlsanity.com/paper/2403.07800/) (2024) - [Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2409.08232/) (2024) - [MRI Scan Synthesis Methods based on Clustering and Pix2Pix](https://mlsanity.com/paper/2312.05176/) (2023) - [Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics](https://mlsanity.com/paper/2408.06075/) (2024) - [Brain Tumour Removing and Missing Modality Generation using 3D WDM](https://mlsanity.com/paper/2411.04630/) (2024) - [Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis](https://mlsanity.com/paper/2411.14684/) (2024) - [CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation](https://mlsanity.com/paper/2309.03320/) (2023) - [CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis](https://mlsanity.com/paper/2406.16074/) (2024) - [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025) - [Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching](https://mlsanity.com/paper/2601.15884/) (2026) - [Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation](https://mlsanity.com/paper/2405.03420/) (2024) - [Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation](https://mlsanity.com/paper/2503.20446/) (2025) - [Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesis](https://mlsanity.com/paper/2502.19390/) (2025) - [DEMIST: Decoupled Multi-stream latent diffusion for Quantitative Myelin Map Synthesis](https://mlsanity.com/paper/2511.12396/) (2025) - [Not Quite Anything: Overcoming SAMs Limitations for 3D Medical Imaging](https://mlsanity.com/paper/2511.19471/) (2025) - [Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training](https://mlsanity.com/paper/2601.16652/) (2026) - [MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation](https://mlsanity.com/paper/2605.16464/) (2026) - [Seeing What Is Actually There: PriVE-Bench and PriVE-Tools for Counterfactual Evaluation of Agentic Visual Evidence in VLMs](https://mlsanity.com/paper/2607.16311/) (2026) - [MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models](https://mlsanity.com/paper/2608.10291/) (2026) ## 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.