Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Creators: name: LaBella, Dominic
name: Baid, Ujjwal
name: Khanna, Omaditya
name: McBurney-Lin, Shan
name: McLean, Ryan
name: Nedelec, Pierre
name: Rashid, Arif
name: Tahon, Nourel Hoda
name: Altes, Talissa
name: Bhalerao, Radhika
name: Dhemesh, Yaseen
name: Godfrey, Devon
name: Hilal, Fathi
name: Floyd, Scott
name: Janas, Anastasia
name: Kazerooni, Anahita Fathi
name: Kirkpatrick, John
name: Kent, Collin
name: Kofler, Florian
name: Leu, Kevin
name: Maleki, Nazanin
name: Menze, Bjoern
name: Pajot, Maxence
name: Reitman, Zachary J.
name: Rudie, Jeffrey D.
name: Saluja, Rachit
name: Velichko, Yury
name: Wang, Chunhao
name: Warman, Pranav
name: Adewole, Maruf
name: Albrecht, Jake
name: Anazodo, Udunna
name: Anwar, Syed Muhammad
name: Bergquist, Timothy
name: Chen, Sully Francis
name: Chung, Verena
name: Chai, Rong
name: Conte, Gian-Marco
name: Dako, Farouk
name: Eddy, James
name: Ezhov, Ivan
name: Khalili, Nastaran
name: Iglesias, Juan Eugenio
name: Jiang, Zhifan
name: Johanson, Elaine
name: Van Leemput, Koen
name: Li, Hongwei Bran
name: Linguraru, Marius George
name: Liu, Xinyang
name: Mahtabfar, Aria
name: Meier, Zeke
name: Moawad, Ahmed W.
name: Mongan, John
name: Piraud, Marie
name: Shinohara, Russell Takeshi
name: Wiggins, Walter F.
name: Abayazeed, Aly H.
name: Akinola, Rachel
name: Jakab, András
name: Bilello, Michel
name: de Verdier, Maria Correia
name: Crivellaro, Priscila
name: Davatzikos, Christos
name: Farahani, Keyvan
name: Freymann, John
name: Hess, Christopher
name: Huang, Raymond
name: Lohmann, Philipp
name: Moassefi, Mana
name: Pease, Matthew W.
name: Vollmuth, Phillipp
name: Sollmann, Nico
name: Diffley, David
name: Nandolia, Khanak K.
name: Warren, Daniel I.
name: Hussain, Ali
name: Fehringer, Pascal
name: Bronstein, Yulia
name: Deptula, Lisa
name: Stein, Evan G.
name: Taherzadeh, Mahsa
name: de Oliveira, Eduardo Portela
name: Haughey, Aoife
name: Kontzialis, Marinos
name: Saba, Luca
name: Turner, Benjamin
name: Brüßeler, Melanie M. T.
name: Ansari, Shehbaz
name: Gkampenis, Athanasios
name: Weiss, David Maximilian
name: Mansour, Aya
name: Shawali, Islam H.
name: Yordanov, Nikolay
name: Stein, Joel M.
name: Hourani, Roula
name: Moshebah, Mohammed Yahya
name: Abouelatta, Ahmed Magdy
name: Rizvi, Tanvir
name: Willms, Klara
name: Martin, Dann C.
name: Okar, Abdullah
name: D'Anna, Gennaro
name: Taha, Ahmed
name: Sharifi, Yasaman
name: Faghani, Shahriar
name: Kite, Dominic
name: Pinho, Marco
name: Haider, Muhammad Ammar
name: Aristizabal, Alejandro
name: Karargyris, Alexandros
name: Kassem, Hasan
name: Pati, Sarthak
name: Sheller, Micah
name: Alonso-Basanta, Michelle
name: Villanueva-Meyer, Javier
name: Rauschecker, Andreas M.
name: Nada, Ayman
name: Aboian, Mariam
name: Flanders, Adam E.
name: Wiestler, Benedikt
name: Bakas, Spyridon
name: Calabrese, Evan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Image and Video Processing (eess.IV)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Machine Learning (cs.LG)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.
description: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2025:003 22 pages, 6 tables, 12 figures, MICCAI, MELBA