학술논문

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) .
Document Type
Academic Journal
Author
Kazerooni AF; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA.; Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.; Khalili N; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Liu X; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.; Haldar D; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, Thomas Jefferson University Hospital, PA, USA.; Jiang Z; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.; Anwar SM; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.; Albrecht J; Sage Bionetworks, USA.; Adewole M; Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria.; Anazodo U; Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada.; Anderson H; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Bagheri S; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Baid U; Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Bergquist T; Sage Bionetworks, USA.; Borja AJ; Department of Neurosurgery at the University of Southern California, CA, USA.; Calabrese E; Department of Radiology, Duke University Medical Center, USA.; Chung V; Sage Bionetworks, USA.; Conte GM; Mayo Clinic, MN, USA.; Dako F; Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Eddy J; Sage Bionetworks, USA.; Ezhov I; Department of Informatics, Technical University Munich, Germany.; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany.; Familiar A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Farahani K; Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.; Haldar S; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Biomedical Engineering Rutgers University, New Brunswick, NJ, USA.; Iglesias JE; Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.; Janas A; Yale University, New Haven, CT, USA.; Johansen E; PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA.; Jones BV; Cincinnati Children's Hospital Medical Center.; Kofler F; Helmholtz AI, Helmholtz Munich, Germany.; LaBella D; Department of Radiation Oncology, Duke University Medical Center, USA.; Lai HA; Department of Radiology, Children's Health Orange County, CA, USA.; Van Leemput K; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.; Li HB; Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.; Maleki N; Yale University, New Haven, CT, USA.; McAllister AS; Department of Radiology, Nationwide Children's Hospital, OH.; Meier Z; Booz Allen Hamilton, McLean, VA, USA.; Menze B; Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland.; Department of Neuroradiology, Technical University of Munich, Munich, Germany.; Moawad AW; Mercy Catholic Medical Center, Darby, PA, USA.; Nandolia KK; Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Rishikesh, India.; Pavaine J; Department of Paediatric Radiology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, University of Manchester, Manchester, UK.; Division of Informatics, Imaging & Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.; Piraud M; Helmholtz AI, Helmholtz Munich, Germany.; Poussaint T; Dana-Farber Brigham Cancer Center & Boston Children's Hospital, Boston, MA, USA.; Prabhu SP; Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.; Reitman Z; Department of Radiation Oncology, Duke University Medical Center, USA.; Rodriguez A; Department of Diagnostic & Interventional Imaging, Neuroradiology section. The University of Texas Health Sciences Center at Houston.; Rudie JD; University of California, San Diego, CA, USA.; Shaikh IS; Beth Israel Deaconess Medical Center, Harvard Medical School, MA, USA.; Shah LM; University of Utah, UT, USA.; Sheth N; Department of Radiology, The Children's Hospital of Philadelphia, PA, USA.; Shinohara RT; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA.; Tu W; College of Arts and Sciences, University of Pennsylvania, PA, USA.; Viswanathan K; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Wang C; Department of Radiation Oncology, Duke University Medical Center, USA.; Ware JB; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Wiestler B; Department of Neuroradiology, Technical University of Munich, Munich, Germany.; Wiggins W; Department of Radiology, Duke University Medical Center, USA.; Zapaishchykova A; Dana-Farber Brigham Cancer Center & Boston Children's Hospital, Boston, MA, USA.; Aboian M; Yale University, New Haven, CT, USA.; Bornhorst M; Brain Tumor Institute, Children's National Hospital, Washington, DC, USA.; de Blank P; Brain Tumor Center, Cincinnati Children's Hospital, Cincinnati, OH, USA.; Deutsch M; Pediatric Neuro-Oncology Program, Nationwide Children's Hospital, Columbus, OH, US.; Fouladi M; Pediatric Neuro-Oncology Program, Nationwide Children's Hospital, Columbus, OH, US.; Hoffman L; Center for Cancer and Blood Disorders, Phoenix Children's Hospital, Phoenix, AZ, US.; Kann B; Dana-Farber Brigham Cancer Center & Boston Children's Hospital, Boston, MA, USA.; Lazow M; Pediatric Neuro-Oncology Program, Nationwide Children's Hospital, Columbus, OH, US.; Mikael L; Pediatric Neuro-Oncology Program, Nationwide Children's Hospital, Columbus, OH, US.; Nabavizadeh A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Packer R; Brain Tumor Institute, Children's National Hospital, Washington, DC, USA.; Resnick A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA.; Rood B; Center for Cancer and Blood Disorders, Children's National Hospital, Washington, DC, USA.; Vossough A; Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.; Department of Radiology, The Children's Hospital of Philadelphia, PA, USA.; Bakas S; Center for AI & Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Linguraru MG; Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA.; Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Source
Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE
Subject
Language
English
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.