The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Anahita Fathi Kazerooni, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Nastaran Khalili, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Xinyang Liu, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.
Debanjan Haldar, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Zhifan Jiang, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.
Syed Muhammed Anwar, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.
Jake Albrecht, Sage Bionetworks, USA.
Maruf Adewole, Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria.
Udunna Anazodo, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada.
Hannah Anderson, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Sina Bagheri, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Ujjwal Baid, Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
Timothy Bergquist, Sage Bionetworks, USA.
Austin J. Borja, Department of Neurosurgery at the University of Southern California, CA, USA.
Evan Calabrese, Department of Radiology, Duke University Medical Center, USA.
Verena Chung, Sage Bionetworks, USA.
Gian-Marco Conte, Mayo Clinic, MN, USA.
Farouk Dako, Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
James Eddy, Sage Bionetworks, USA.
Ivan Ezhov, Department of Informatics, Technical University Munich, Germany.
Ariana Familiar, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Keyvan Farahani, Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Shuvanjan Haldar, Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Juan Eugenio Iglesias, Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
Anastasia Janas, Yale University, New Haven, CT, USA.
Elaine Johansen, PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Blaise V. Jones, Cincinnati Children's Hospital Medical Center.
Florian Kofler, Helmholtz AI, Helmholtz Munich, Germany.
Dominic LaBella, Department of Radiation Oncology, Duke University Medical Center, USA.
Hollie Anne Lai, Department of Radiology, Children's Health Orange County, CA, USA.
Koen Van Leemput, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
Hongwei Bran Li, Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.

Document Type

Journal Article

Publication Date

1-4-2024

Journal

ArXiv

Keywords

AI; BraTS; BraTS-PEDs; artificial intelligence; brain; challenge; deep learning; machine learning; pediatric; segmentation; tumor; volume

Abstract

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Department

Radiology

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