BraTS Orchestrator: Unified Brain Tumor Analysis

• BraTS Orchestrator is an open-source Python framework that consolidates state-of-the-art deep learning models for brain tumor segmentation and synthesis.
• It simplifies complex MRI processing workflows with modular APIs and Docker integration, enabling clinical and research users with minimal coding skills.
• The framework supports ensemble fusion and standardized metrics like the Dice coefficient to ensure reproducible, clinically relevant outcomes.

The BraTS Orchestrator is an open-source Python package designed to democratize and accelerate the application of state-of-the-art brain tumor image analysis methods arising from the BraTS challenge ecosystem. Operating as a modular, API-driven framework, the BraTS orchestrator abstracts the complexities of preprocessing, inference, and post-processing for MRI-based segmentation and synthesis algorithms, targeting both clinical and research users with minimal programming expertise. It integrates, under a common interface, leading deep learning models from multiple BraTS tasks (such as segmentation of glioma, meningioma, metastasis, pediatric brain tumors, and synthesis tasks like missing modality generation), providing streamlined access, reproducible inference, and ensemble capabilities for a broad spectrum of applications in neuro-oncology and neuro-radiology (Kofler et al., 13 Jun 2025).

1. Purpose and Rationale

The central goal of the BraTS orchestrator is to bridge the gap between advanced research methodologies—often accessible only to machine learning experts—and their practical deployment in clinical and translational settings. Despite broad adoption of the BraTS challenge datasets as benchmarks across machine learning, post-challenge model uptake in clinical/research workflows has been limited. By consolidating winning algorithms under a unified, user-friendly API with minimal dependencies, the orchestrator aims to lower technical barriers and standardize robust, reproducible pipelines for brain tumor analysis.

2. Core Features and Supported Tasks

The orchestrator exposes a wide array of algorithms, reflecting the evolving landscape of BraTS challenges:

• Tumor Segmentation: Algorithms for glioma (high- and low-grade), meningioma, metastasis, pediatric tumors, and SSA glioma are available, typically leveraging architectures such as nnU-Net, Swin UNETR, and MedNeXt, among others.
• Image Synthesis: Supports tasks like missing modality synthesis and lesion inpainting, e.g., using GAN-based pipelines for inferring T2-FLAIR from available T1 and T2w images or for reconstructing healthy tissue in lesioned regions.
• Segmentation Fusion: Incorporates ensemble techniques (e.g., majority voting, SIMPLE fusion) that combine the strengths of multiple candidate segmentations to provide consensus outputs, building on prior BraTS Toolkit strategies.
• Preprocessing: Integrates the BrainLesion suite for critical operations such as atlas-based registration (MNI152, SRI24), skull stripping, and defacing. The pipeline ensures compatibility with major neuroimaging file formats (NIfTI), with future support planned for DICOM.

A generalized workflow is visualized below:

Input MRI (NIfTI) → Preprocessing (Registration, Skull Strip, Deface) ↓ Model Inference (Segmentation or Synthesis) ↓ Post-processing (optional) → Ensemble Fusion (optional) ↓ Output (Segmentation map or Synthesized image)

3. Technical Implementation and User Accessibility

Implemented as a Python package, the orchestrator leverages Docker containerization to ensure reproducibility and ease of deployment across Linux, Windows, and macOS environments (subject to administrative rights). The API abstracts deep learning pipeline details, enabling non-expert users to configure, execute, and visualize results with minimal code.

Key technical elements:

• Docker Integration: Ensures reproducible inference runs irrespective of local library versions or hardware heterogeneity.
• Tutorials and Documentation: Step-by-step guides are provided via the project’s GitHub, targeting both command-line and API-based usage scenarios.
• Modularity: Users can compose custom workflows by selecting specific preprocessing steps, models, and postprocessing modules, fostering rapid experimentation and adoption in diverse environments.

4. Performance Metrics and Clinical Integration

The orchestrator implements performance metrics in line with BraTS evaluation protocols, such as the Dice similarity coefficient (DSC):

$$\text{DSC} = \frac{2|\text{Prediction} \cap \text{GroundTruth}|}{|\text{Prediction}| + |\text{GroundTruth}|}$$

This metric quantifies overlap for segmentation tasks, critical for both validation and clinical translation. By providing reliable, validated segmentations, the orchestrator supports clinical applications including radiotherapy treatment planning, quantitative tumor monitoring, and multi-center research studies.

It is being further developed to support:

• Native Space Inference: The current design processes images in atlas space. Plans are in place to "invert" registration and skull stripping transformations, allowing output segmentations to correctly map to original patient coordinates—essential for direct integration in the radiological workflow.
• DICOM Compatibility: While currently NIfTI-centric, roadmap includes parsing native DICOM input/output for seamless PACS integration.

5. Consensus, Dissemination, and Extensibility

A major contribution of the orchestrator is to centralize top-performing algorithms across BraTS tasks and provide an extensible point for the community to add successive challenge winners. This sustained consolidation prevents fragmentation; new segmentation (or synthesis) approaches can be rapidly disseminated to the biomedical community and incorporated into pipelines, enabling an iterative standardization of best practices.

Supported ensemble strategies allow users to combine the outputs of multiple models for robust consensus, which is particularly important in high-variance or clinically ambiguous cases (e.g., post-surgical volumes, rare tumor histotypes).

6. Future Directions

Planned enhancements include:

• Graphical User Interface (GUI): To further reduce user-side complexity, future versions will include GUI-driven interaction, obviating the need for any programming or command-line invocation.
• Continuous Integration of Challenge Winners: As new, more performant algorithms emerge from annual BraTS challenges, they will be added to the orchestrator’s model zoo with minimal adoption latency.
• Clinical Pipeline Integration: Closer coupling with neuro-oncology clinical IT infrastructure, including DICOM-native workflows and reporting tools, is under consideration.

7. Significance for the Neuroimaging Community

By democratizing algorithm access, enforcing reproducibility via containerization, and providing user-centric documentation, the BraTS orchestrator stands as an enabling infrastructure for routine, large-scale, and multi-center neuro-oncology research and clinical deployment. It is positioned as a central translational engine for disseminating state-of-the-art methods from the BraTS challenge to real-world applications without algorithmic or engineering impediments (Kofler et al., 13 Jun 2025).

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Editor’s term: "Orchestrator" as used here refers specifically to this Python-based, API-driven enabling framework, as distinct from the broader process of "orchestrating" segmentation tasks in the neuroimaging literature.