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 post-operative
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 post-operative site. Target volume annotations
adhere to established radiotherapy planning protocols, ensuring consistency
across cases and institutions. For pre-operative meningiomas, the target volume
encompasses the entire GTV and associated nodular dural tail, while for
post-operative 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 the
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.

通过使用最大的已知多机构放射治疗计划脑 MRI 数据集，对具有完整或术后脑膜瘤的患者进行放射治疗，本研究旨在推进自动化分割算法。该数据集包含脱敏的三维增强 T1 加权放射治疗计划 MRI 及其相应的标注目标体积，能够准确分割肿瘤并提供个体化治疗，预计将显著改善患者预后。