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The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Dominic LaBella, Maruf Adewole, Michelle Alonso-Basanta, Talissa Altes, Syed Muhammad Anwar, Ujjwal Baid, Timothy Bergquist, Radhika Bhalerao, Sully Chen, Verena Chung, Gian-Marco Conte, Farouk Dako, James Eddy, Ivan Ezhov, Devon Godfrey, Fathi Hilal, Ariana Familiar, Keyvan Farahani, Juan Eugenio Iglesias, Zhifan Jiang, Elaine Johanson, Anahita Fathi Kazerooni, Collin Kent, John Kirkpatrick, Florian Kofler, Koen Van Leemput, Hongwei Bran Li, Xinyang Liu, Aria Mahtabfar, Shan McBurney-Lin, Ryan McLean, Zeke Meier, Ahmed W Moawad, John Mongan, Pierre Nedelec, Maxence Pajot, Marie Piraud, Arif Rashid, Zachary Reitman, Russell Takeshi Shinohara, Yury Velichko, Chunhao Wang, Pranav Warman, Walter Wiggins, Mariam Aboian, Jake Albrecht, Udunna Anazodo, Spyridon Bakas, Adam Flanders, Anastasia Janas, Goldey Khanna, Marius George Linguraru, Bjoern Menze, Ayman Nada, Andreas M Rauschecker, Jeff Rudie, Nourel Hoda Tahon, Javier Villanueva-Meyer, Benedikt Wiestler, Evan Calabrese

TL;DR

The paper addresses the lack of noninvasive, quantitative tools for intracranial meningioma assessment and establishes the BraTS 2023 meningioma challenge to develop automated multi-compartment segmentation on multiparametric MRI. It details an annotation-rich data workflow, including nnU-Net-based presegmentation, manual corrections with ITK-SNAP, and expert approval, evaluated with Dice and Hausdorff metrics across a large, multi-site dataset. Key contributions include a standardized annotation protocol, a large, heterogeneous mpMRI dataset, and publicly available model weights to promote reproducibility and generalizability. The work aims to improve clinical workflows in surgical/radiation planning and longitudinal monitoring, and it sets the stage for future efforts in noninvasive grading, recurrence prediction, and broader applicability across postoperative and extracranial meningiomas.

Abstract

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

TL;DR

The paper addresses the lack of noninvasive, quantitative tools for intracranial meningioma assessment and establishes the BraTS 2023 meningioma challenge to develop automated multi-compartment segmentation on multiparametric MRI. It details an annotation-rich data workflow, including nnU-Net-based presegmentation, manual corrections with ITK-SNAP, and expert approval, evaluated with Dice and Hausdorff metrics across a large, multi-site dataset. Key contributions include a standardized annotation protocol, a large, heterogeneous mpMRI dataset, and publicly available model weights to promote reproducibility and generalizability. The work aims to improve clinical workflows in surgical/radiation planning and longitudinal monitoring, and it sets the stage for future efforts in noninvasive grading, recurrence prediction, and broader applicability across postoperative and extracranial meningiomas.

Abstract

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.
Paper Structure (16 sections, 1 equation, 4 figures)

This paper contains 16 sections, 1 equation, 4 figures.

Table of Contents

  1. Introduction
  2. Materials & Methods
  3. Defining intracranial meningioma
  4. Defining Meningioma MRI Sub-Components
  5. Data Description
  6. Participating sites
  7. Image data preprocessing
  8. Pre-segmentation
  9. Manual corrections
  10. Common errors of automated segmentation
  11. Discussion
  12. Potential benefits of the challenge
  13. Clinical relevance
  14. Limitations of the challenge
  15. Goals for future challenges
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Example of an en plaque meningioma on a T1Gd coronal image.
  • Figure 2: Example of a meningioma dural tail on a T1Gd axial image.
  • Figure 3: Meningioma sub-regions considered in the RSNA-ASNR-MICCAI BraTS 2023 Meningioma Challenge. Image panels with the tumor sub-regions annotated in the different mpMRI scans. The image panels A-C denote the regions considered for the performance evaluation of the participating algorithms and specifically highlight: the enhancing tumor (blue) visible in a T1Gd scan, (panel A); the non-enhancing tumor core (red) visible in a T1Gd scan, (panel B); and the surrounding FLAIR hyperintensity (green) visible in a T2-FLAIR scan. Panel D depicts the combined segmentations generating the final tumor sub-region labels, as provided to the BraTS 2023 meningioma challenge participants: enhancing tumor (blue), non-enhancing tumor core (red), and edema (green).
  • Figure 4: Common errors expected from automated segmentation of meningioma sub-regions.