The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Abstract
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care.
Cite
@article{arxiv.2405.18368,
title = {The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI},
author = {Maria Correia de Verdier and Rachit Saluja and Louis Gagnon and Dominic LaBella and Ujjwall Baid and Nourel Hoda Tahon and Martha Foltyn-Dumitru and Jikai Zhang and Maram Alafif and Saif Baig and Ken Chang and Gennaro D'Anna and Lisa Deptula and Diviya Gupta and Muhammad Ammar Haider and Ali Hussain and Michael Iv and Marinos Kontzialis and Paul Manning and Farzan Moodi and Teresa Nunes and Aaron Simon and Nico Sollmann and David Vu and Maruf Adewole and Jake Albrecht and Udunna Anazodo and Rongrong Chai and Verena Chung and Shahriar Faghani and Keyvan Farahani and Anahita Fathi Kazerooni and Eugenio Iglesias and Florian Kofler and Hongwei Li and Marius George Linguraru and Bjoern Menze and Ahmed W. Moawad and Yury Velichko and Benedikt Wiestler and Talissa Altes and Patil Basavasagar and Martin Bendszus and Gianluca Brugnara and Jaeyoung Cho and Yaseen Dhemesh and Brandon K. K. Fields and Filip Garrett and Jaime Gass and Lubomir Hadjiiski and Jona Hattangadi-Gluth and Christopher Hess and Jessica L. Houk and Edvin Isufi and Lester J. Layfield and George Mastorakos and John Mongan and Pierre Nedelec and Uyen Nguyen and Sebastian Oliva and Matthew W. Pease and Aditya Rastogi and Jason Sinclair and Robert X. Smith and Leo P. Sugrue and Jonathan Thacker and Igor Vidic and Javier Villanueva-Meyer and Nathan S. White and Mariam Aboian and Gian Marco Conte and Anders Dale and Mert R. Sabuncu and Tyler M. Seibert and Brent Weinberg and Aly Abayazeed and Raymond Huang and Sevcan Turk and Andreas M. Rauschecker and Nikdokht Farid and Philipp Vollmuth and Ayman Nada and Spyridon Bakas and Evan Calabrese and Jeffrey D. Rudie},
journal= {arXiv preprint arXiv:2405.18368},
year = {2024}
}
Comments
10 pages, 4 figures, 1 table