Scientific Data (May 2024)
A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation
- Dominic LaBella,
- Omaditya Khanna,
- Shan McBurney-Lin,
- Ryan Mclean,
- Pierre Nedelec,
- Arif S. Rashid,
- Nourel hoda Tahon,
- Talissa Altes,
- Ujjwal Baid,
- Radhika Bhalerao,
- Yaseen Dhemesh,
- Scott Floyd,
- Devon Godfrey,
- Fathi Hilal,
- Anastasia Janas,
- Anahita Kazerooni,
- Collin Kent,
- John Kirkpatrick,
- Florian Kofler,
- Kevin Leu,
- Nazanin Maleki,
- Bjoern Menze,
- Maxence Pajot,
- Zachary J. Reitman,
- Jeffrey D. Rudie,
- Rachit Saluja,
- Yury Velichko,
- Chunhao Wang,
- Pranav I. Warman,
- Nico Sollmann,
- David Diffley,
- Khanak K. Nandolia,
- Daniel I Warren,
- Ali Hussain,
- John Pascal Fehringer,
- Yulia Bronstein,
- Lisa Deptula,
- Evan G. Stein,
- Mahsa Taherzadeh,
- Eduardo Portela de Oliveira,
- Aoife Haughey,
- Marinos Kontzialis,
- Luca Saba,
- Benjamin Turner,
- Melanie M. T. Brüßeler,
- Shehbaz Ansari,
- Athanasios Gkampenis,
- David Maximilian Weiss,
- Aya Mansour,
- Islam H. Shawali,
- Nikolay Yordanov,
- Joel M. Stein,
- Roula Hourani,
- Mohammed Yahya Moshebah,
- Ahmed Magdy Abouelatta,
- Tanvir Rizvi,
- Klara Willms,
- Dann C. Martin,
- Abdullah Okar,
- Gennaro D’Anna,
- Ahmed Taha,
- Yasaman Sharifi,
- Shahriar Faghani,
- Dominic Kite,
- Marco Pinho,
- Muhammad Ammar Haider,
- Michelle Alonso-Basanta,
- Javier Villanueva-Meyer,
- Andreas M. Rauschecker,
- Ayman Nada,
- Mariam Aboian,
- Adam Flanders,
- Spyridon Bakas,
- Evan Calabrese
Affiliations
- Dominic LaBella
- Department of Radiation Oncology, Duke University Medical Center
- Omaditya Khanna
- Department of Neurosurgery, Thomas Jefferson University
- Shan McBurney-Lin
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Ryan Mclean
- Yale University
- Pierre Nedelec
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Arif S. Rashid
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania
- Nourel hoda Tahon
- University of Missouri
- Talissa Altes
- University of Missouri
- Ujjwal Baid
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University
- Radhika Bhalerao
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Yaseen Dhemesh
- University of Missouri
- Scott Floyd
- Department of Radiation Oncology, Duke University Medical Center
- Devon Godfrey
- Department of Radiation Oncology, Duke University Medical Center
- Fathi Hilal
- University of Missouri
- Anastasia Janas
- Yale University
- Anahita Kazerooni
- Center for Data-Driven Discovery in Biomedicine (D3b), The Children’s Hospital of Philadelphia
- Collin Kent
- Department of Radiation Oncology, Duke University Medical Center
- John Kirkpatrick
- Department of Radiation Oncology, Duke University Medical Center
- Florian Kofler
- Helmholtz AI, Helmholtz Munich
- Kevin Leu
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Nazanin Maleki
- Yale University
- Bjoern Menze
- University of Zurich
- Maxence Pajot
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Zachary J. Reitman
- Department of Radiation Oncology, Duke University Medical Center
- Jeffrey D. Rudie
- Department of Radiology, University of California San Diego
- Rachit Saluja
- Department of Radiology, Cornell University
- Yury Velichko
- Department of Radiology, Northwestern University
- Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center
- Pranav I. Warman
- Duke University Medical Center, School of Medicine
- Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich
- David Diffley
- Khanak K. Nandolia
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences
- Daniel I Warren
- Department of Neuroradiology, Washington University
- Ali Hussain
- University of Rochester Medical Center
- John Pascal Fehringer
- Faculty of Medicine, Jena University Hospital, Friedrich Schiller University Jena
- Yulia Bronstein
- vRad (Radiology Partners)
- Lisa Deptula
- Ross University School of Medicine
- Evan G. Stein
- Department of Radiology, New York University Grossman School of Medicine
- Mahsa Taherzadeh
- Department of Radiology, Arad Hospital
- Eduardo Portela de Oliveira
- Department of Radiology, Faculty of Medicine, University of Ottawa
- Aoife Haughey
- Department of Neuroradiology, JDMI, University of Toronto
- Marinos Kontzialis
- Department of Radiology, Cornell University
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria of Cagliari-Polo di Monserrato
- Benjamin Turner
- Department of Radiology, Leeds General Infirmary
- Melanie M. T. Brüßeler
- Ludwig Maximilians University
- Shehbaz Ansari
- Rush University Medical Center
- Athanasios Gkampenis
- Department of Neurosurgery, University Hospital of Ioannina
- David Maximilian Weiss
- Department of Neuroradiology, University Hospital Essen
- Aya Mansour
- Egyptian Ministry of Health
- Islam H. Shawali
- Department of Radiology, Kasr Alainy, Cairo University
- Nikolay Yordanov
- Faculty of Medicine, Medical University of Sofia
- Joel M. Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
- Roula Hourani
- Department of Radiology, American University of Beirut Medical center
- Mohammed Yahya Moshebah
- Radiology Department, King Faisal Medical City
- Ahmed Magdy Abouelatta
- Department of Diagnostic and Interventional Radiology, Cairo University
- Tanvir Rizvi
- Department of Radiology and Medical Imaging, University of Virginia Health
- Klara Willms
- Yale University
- Dann C. Martin
- Department of Radiology and Radiologic Sciences, Vanderbilt University Medical Center
- Abdullah Okar
- Faculty of Medicine, Hamburg University
- Gennaro D’Anna
- Neuroimaging Unit, ASST Ovest Milanese, Legnano
- Ahmed Taha
- University of Manitoba
- Yasaman Sharifi
- Department of Radiology, School of Medicine, Iran University of Medical Sciences
- Shahriar Faghani
- Radiology Informatics Lab, Department of Radiology, Mayo Clinic
- Dominic Kite
- Department of Radiology, University Hospitals Bristol and Weston NHS Foundation Trust
- Marco Pinho
- Department of Radiology, University of Texas Southwestern Medical Center
- Muhammad Ammar Haider
- CMH Lahore Medical College
- Michelle Alonso-Basanta
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania
- Javier Villanueva-Meyer
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Andreas M. Rauschecker
- Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF)
- Ayman Nada
- University of Missouri
- Mariam Aboian
- Department of Radiology, Children’s Hospital of Philadelphia (CHOP)
- Adam Flanders
- Department of Radiology, Thomas Jefferson University
- Spyridon Bakas
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University
- Evan Calabrese
- Department of Radiology, Duke University Medical Center
- DOI
- https://doi.org/10.1038/s41597-024-03350-9
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 8
Abstract
Abstract Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.
