# BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

arXiv: https://arxiv.org/abs/2506.13807 · HTML: https://mlsanity.com/paper/2506.13807/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [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)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling](https://mlsanity.com/paper/2111.04090/) (2021)
- [Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge](https://mlsanity.com/paper/2310.04110/) (2023)
- [Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks](https://mlsanity.com/paper/2403.09262/) (2024)
- [Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2409.08232/) (2024)
- [BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions](https://mlsanity.com/paper/2405.10871/) (2024)
- [Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data](https://mlsanity.com/paper/2412.04111/) (2024)
- [Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology](https://mlsanity.com/paper/2002.09628/) (2020)
- [MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework](https://mlsanity.com/paper/2407.21343/) (2024)
- [3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge](https://mlsanity.com/paper/2403.15735/) (2024)
- [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024)
- [Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics](https://mlsanity.com/paper/2411.15872/) (2024)
- [Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models](https://mlsanity.com/paper/2409.08143/) (2024)
- [Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis](https://mlsanity.com/paper/2410.18698/) (2024)
- [Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade](https://mlsanity.com/paper/2410.14020/) (2024)

## Built on by (most cited first, 8 of 8)

- [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026)
- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning](https://mlsanity.com/paper/2509.13360/) (2025)
- [TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth](https://mlsanity.com/paper/2603.04058/) (2026)
- [Post-Processing Methods for Improving Accuracy in MRI Inpainting](https://mlsanity.com/paper/2510.15282/) (2025)
- [Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques](https://mlsanity.com/paper/2512.14937/) (2025)
- [SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI](https://mlsanity.com/paper/2509.16019/) (2025)
- [MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models](https://mlsanity.com/paper/2608.10291/) (2026)

## Abstract (arXiv)

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically relevant tasks. However, despite its success and popularity, algorithms and models developed through BraTS have seen limited adoption in both scientific and clinical communities. To accelerate their dissemination, we introduce BraTS orchestrator, an open-source Python package that provides seamless access to state-of-the-art segmentation and synthesis algorithms for diverse brain tumors from the BraTS challenge ecosystem. Available on GitHub (https://github.com/BrainLesion/BraTS), the package features intuitive tutorials designed for users with minimal programming experience, enabling both researchers and clinicians to easily deploy winning BraTS algorithms for inference. By abstracting the complexities of modern deep learning, BraTS orchestrator democratizes access to the specialized knowledge developed within the BraTS community, making these advances readily available to broader neuro-radiology and neuro-oncology audiences.