The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

作者：Maria Correia de Verdier, Rachit Saluja, L. G. Gagnon, Dominic LaBella, Ujjwall Baid, Nourel Hoda Tahon, Martha Foltyn‐Dumitru, Jikai Zhang, Maram Alafif, Saif Baig, Ken Chang, Gennaro D’Anna, Lisa Deptula, Diviya Gupta, Muhammad Ammar Haider, Ali Hussain, Michael Iv, Marinos Kontzialis, Paul Manning, Farzan Moodi, Teresa Nunes, Aaron B. Simon, Nico Sollmann, David M. Vu, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Rongrong Chai, Verena Chung, Shahriar Faghani, Keyvan Farahani, Anahita Fathi Kazerooni, Eugenio Iglesias, Florian Kofler, Hongwei Li, Marius George Linguraru, Bjoern Menze, Ahmed W. Moawad, Yury Velichko, Benedikt Wiestler, Talissa A. Altes, Patil Basavasagar, Martin Bendszus, Gianluca Brugnara, Jaeyoung Cho, Yaseen Dhemesh, Brandon K.K. Fields, Filip Garrett, Jaime Gass, Lubomir M. Hadjiiski, Jona A. Hattangadi‐Gluth, Christopher P. Hess, Jessica L. Houk, Edvin Isufi, Lester J. Layfield, George Mastorakos, John Mongan, Pierre Nedelec, Uyen Trang Nguyen, Sebastián Oliva, Matthew Pease, Aditya Rastogi, Jason Sinclair, Robert X. Smith, Leo P. Sugrue, Jonathan Thacker, Igor Vidić, Javier Villanueva‐Meyer, Nathan S. White, Mariam Aboian, Gian Marco Conte, Anders Dale, Mert R. Sabuncu, Tyler M. Seibert, Brent D. Weinberg, Aly Abayazeed, Raymond Y. Huang, Sevcan Türk, Andreas M. Rauschecker, Nikdokht Farid, Vollmuth, Philipp, Ayman Nada, Spyridon Bakas, Evan Calabrese, Jeffrey D. Rudie · 发表于：arXiv (Cornell University) · 年份：2024 · DOI：10.48550/arxiv.2405.18368 · 被引用次数：17 · 研究领域：Brain Tumor Detection and Classification、Radiomics and Machine Learning in Medical Imaging、Glioma Diagnosis and Treatment

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.