A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation

• Published in: Scientific data Vol. 11; no. 1; p. 496
• Main Authors: LaBella, Dominic, Khanna, Omaditya, McBurney-Lin, Shan, Mclean, Ryan, Nedelec, Pierre, Rashid, Arif S., Tahon, Nourel hoda, Altes, Talissa, Baid, Ujjwal, Bhalerao, Radhika, Dhemesh, Yaseen, Floyd, Scott, Godfrey, Devon, Hilal, Fathi, Janas, Anastasia, Kazerooni, Anahita, Kent, Collin, Kirkpatrick, John, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav I., Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I, Hussain, Ali, Fehringer, John Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, Portela de Oliveira, Eduardo, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D’Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam, Bakas, Spyridon, Calabrese, Evan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15-05-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/575; 692/698/1688/64; 692/699/67/1922; 692/700/1421/65; 706/648/697/129; Aged; Automation; Computational neuroscience; Data Descriptor; Datasets; Female; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Male; Meningeal Neoplasms - diagnostic imaging; Meningioma; Meningioma - diagnostic imaging; Middle Aged; Morbidity; multidisciplinary; Neuroimaging; Science; Science (multidisciplinary); Tumors
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-03350-9

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.