mlsanityData to 14 Oct 2026

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

arXiv 2506.13807 ↗First posted 13 Jun 2025eess.IV cs.AI cs.CVcited by 8

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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.

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

24 of 52 references.

References · 24

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  4. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  5. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  6. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 328
  7. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
  8. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 162
  9. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 100
  10. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 28
  11. 2021Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modelingcited by 19
  12. 2023Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challengecited by 19
12 more
  1. 2024Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Taskscited by 12
  2. 2024Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imagingcited by 7
  3. 2024BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regionscited by 6
  4. 2024Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Datacited by 6
  5. 2020Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncologycited by 5
  6. 2024MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Frameworkcited by 5
  7. 20243D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challengecited by 4
  8. 2024Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentationcited by 4
  9. 2024Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatricscited by 4
  10. 2024Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Modelscited by 2
  11. 2024Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosiscited by 2
  12. 2024Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascadecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
  2. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 3
  3. 2025PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planningcited by 2
  4. 2026TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growthcited by 2
  5. 2025Post-Processing Methods for Improving Accuracy in MRI Inpaintingcited by 1
  6. 2025Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniquescited by 1
  7. 2025SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRIcited by 0
  8. 2026MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Modelscited by 0