Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

2024-05-28

arXiv.org

AI Summary

Automated target segmentation in meningioma radiotherapy suffers from low accuracy and poor generalizability across multi-institutional settings. Method: We constructed the first large-scale, multi-center radiotherapy planning–grade brain MRI dataset (n=750), featuring expert annotations of gross tumor volume (GTV) and postoperative high-risk regions, covering both preoperative (including dural tail sign) and postoperative scenarios. We established a novel, radiotherapy protocol–compliant single-label target definition standard, jointly validated by neuroradiology and radiation oncology experts. Using 3D contrast-enhanced T1-weighted MRI, we developed a containerized deep learning segmentation model, evaluated primarily via lesion-level Dice coefficient and 95% Hausdorff distance. Contribution/Results: The framework significantly improves segmentation consistency and clinical interpretability, delivering a reproducible, deployable technical foundation for precise, individualized radiotherapy planning.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label"target volume"representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes.

Problem

• Advance automated segmentation for meningioma radiotherapy planning
• Evaluate algorithms using largest multi-institutional MRI dataset
• Improve precision in tumor segmentation for tailored treatment

Innovation

• Largest multi-institutional MRI dataset for meningioma
• Automated segmentation models using 3D T1-weighted MRI
• Modified lesion-wise DSC and 95HD for evaluation