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

Automated Segmentation of Meningioma for Radiotherapy Planning: BraTS-MEN-RT Challenge

The paper presents an in-depth overview of the 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge. This initiative aims to generate a benchmark dataset for the automated segmentation of meningioma gross tumor volume (GTV) in radiotherapy planning MRI. The challenge represents a significant shift towards relevant and practical applications in clinical workflows, focusing on enhancing the accuracy of tumor volume delineation for radiotherapy.

Background and Motivation

Meningioma is the predominant form of primary intracranial tumors, constituting 40.8% of all CNS tumors. Accurate segmentation of GTV and clinical target volume (CTV) is essential for effective radiotherapy planning. The EORTC 22042-026042 and RTOG 0539 studies provide varying definitions for GTV and CTV, highlighting the diversity and complexity of segmentation practices. Despite the clinical importance, the current automated methods for meningioma segmentation, particularly in post-operative contexts, are limited and underexplored. Prior BraTS challenges have primarily focused on pre-operative tumors, often excluding clinically useful features and employing extensive preprocessing that diminishes clinical utility.

Methods

Data Description

The study leverages approximately 700 radiotherapy planning MRI scans from six academic medical centers in the US. The images include either pre-operative or post-operative settings, typically focusing on 3D post-contrast T1-weighted imaging (T1c) in the native acquisition space. Automatic defacing algorithms are employed to anonymize patient data while preserving extracranial structures, a notable improvement over previous skull-stripping methods.

Target Volume Definitions

For pre-operative settings, the target label consists of the visible tumor portion on T1c MRI. Post-operative settings involve the resection bed and any residual enhancing tumor. These annotation protocols were rigorously reviewed and agreed upon by a consortium of board-certified radiation oncologists and neuroradiologists. Notably, all visible intracranial meningiomas are labeled, providing comprehensive segmentation regardless of whether the tumors were treated in the real-world clinical scenario.

Image Preprocessing and Manual Corrections

The images are converted from DICOM to NIfTI format, followed by AFNI's automated defacing. A substantial manual quality control process ensures the inclusion of all meningioma structures, even if partially present in the defaced images. Institutional GTV labels are reviewed and corrected manually to conform to the challenge's rigorous standards. Furthermore, for cases without GTV labels, a deep convolutional neural network (nnUnet) pre-segmentation model is employed, iteratively refined based on additional BraTS-MEN-RT cases.

Discussion

Clinical Relevance

Automated segmentation models developed from the BraTS-MEN-RT challenge could significantly streamline the generation of radiotherapy treatment plans. These models offer consistent and objective delineation of tumor volumes, crucial for precise and effective radiotherapy. Such standardization can enhance treatment reproducibility and quality, reducing the potential for manual segmentation errors.

Future Directions

Participants in the challenge are encouraged to utilize additional datasets, such as the 1424 pre-operative meningioma cases from the 2023 BraTS-MEN challenge. However, practitioners must adapt preprocessing techniques to reconcile differences in image spaces and sequences. Enhancing the dataset with multimodal imaging, including CT and PET, could provide more holistic insights into tumor characteristics and improve segmentation accuracy.

Limitations and Recommendations

The reliance on a single T1c sequence might limit the ability to capture comprehensive tumor heterogeneity. Future challenges should incorporate multimodal imaging data to provide more accurate and robust models. Moreover, variability in MRI acquisition protocols across institutions may introduce segmentation biases, necessitating further standardization efforts.

Integrating these automated tools into clinical practice involves various hurdles, including the need for clinician training and validation in diverse clinical settings. Despite these challenges, the open-source release of the segmentation models promises substantial opportunities for both academic and industry advancements.

Conclusion

The BraTS-MEN-RT challenge represents a pivotal step towards practically applicable automated segmentation models for meningioma in radiotherapy planning. By fostering a common benchmark and encouraging open-source development, the initiative paves the way for significant advancements in tumor segmentation, potentially improving radiotherapy outcomes and clinical workflow efficiency.

This research underscores the importance of continued development and validation of automated segmentation models, urging a shift towards integrating multimodal imaging data and addressing variability in clinical data acquisition methodologies. Future challenges will benefit from these insights, aiming to enhance the precision and effectiveness of radiotherapy treatments for various intracranial tumors.