Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Authors:
Dominic LaBella,
Valeriia Abramova,
Mehdi Astaraki,
Andre Ferreira,
Zhifan Jiang,
Mason C. Cleveland,
Ramandeep Kang,
Uma M. Lal-Trehan Estrada,
Cansu Yalcin,
Rachika E. Hamadache,
Clara Lisazo,
Adrià Casamitjana,
Joaquim Salvi,
Arnau Oliver,
Xavier Lladó,
Iuliana Toma-Dasu,
Tiago Jesus,
Behrus Puladi,
Jens Kleesiek,
Victor Alves,
Jan Egger,
Daniel Capellán-Martín,
Abhijeet Parida,
Austin Tapp,
Xinyang Liu
, et al. (80 additional authors not shown)
Abstract:
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 radiosu…
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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.
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Submitted 21 July, 2025; v1 submitted 28 May, 2024;
originally announced May 2024.
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Authors:
Hoo-Chang Shin,
Neil A Tenenholtz,
Jameson K Rogers,
Christopher G Schwarz,
Matthew L Senjem,
Jeffrey L Gunter,
Katherine Andriole,
Mark Michalski
Abstract:
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly av…
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Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.
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Submitted 13 September, 2018; v1 submitted 26 July, 2018;
originally announced July 2018.