Paper

Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge

Vercauteren, Tom, Vollmuth, Phillipp, Oliver, Arnau, Kim, Albert E, Huang, Christina, Yalcin, Cansu, Wiestler, Benedikt, LaBella, Dominic, Chung, Verena, Adewole, Maruf, Floyd, Scott R, Schumacher, Katherine, Kazerooni, Anahita Fathi, Chia, Kazumi, Gerstner, Elizabeth R, Menze, Bjoern, Tiago Jesus, Leu, Justin, Correia de Verdier, Maria, Syed Muhammad Anwar, Maleki, Nazanin, Chai, Rong, Egger, Jan, Sachdev, Sean, Ivory, Marina, Toma-Dasu, Iuliana, Gian-Marco Conte, Kirkpatrick, John P, Baid, Ujjwal, McNeal, Thomas N, Nikdokht Farid, Bridge, Christopher P, Ferreira, Andre, Tapp, Austin, Taylor, Peter, Moawad, Ahmed W, Abramova, Valeriia, Shapey, Jonathan, Kleesiek, Jens, Rauschecker, Andreas M, Viera, Maya, Warman, Pranav, Jakab, András, Salvi, Joaquim, Kassem, Hasan, Huang, Raymond, Astaraki, Mehdi, Lladó, Xavier, Capellán-Martín, Daniel, Pati, Sarthak, Cleveland, Mason C, McCall, Owen, Albrecht, Jake, Lohmann, Philipp, Reitman, Zachary J, Calabrese, Evan, Kang, Ramandeep, Hongwei Bran Li, Barfoot, Theodore, Jiang, Zhifan, Al-Salihi, Omar, Moassefi, Mana, Lal-Trehan Estrada, Uma M, Nedelec, Pierre, Pease, Matth

2025

Overview

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 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 included 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 post-operative site. Target volume annotations adhered to established radiotherapy planning protocols, ensuring consistency across cases and institutions, and were approved by expert neuroradiologists and radiation oncologists. Six participating teams developed, containerized, and evaluated automated segmentation models using this comprehensive dataset. Team rankings were assessed using a modified lesion-wise Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95HD). The best reported average lesion-wise DSC and 95HD was 0.815 and 26.92 mm, respectively. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. We describe the design and results from the BraTS-MEN-RT challenge.

• Publisher: Cornell University Library, arXiv.org
• Subject: Annotations / Automation / Brain / Brain cancer / Datasets / Labels / Lesions / Metric space / Radiation therapy / Segmentation / Tumors