Summary of Brain Tumor Segmentation (brats) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation, by Dominic Labella et al.
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation
by Dominic LaBella, Katherine Schumacher, Michael Mix, Kevin Leu, Shan McBurney-Lin, Pierre Nedelec, Javier Villanueva-Meyer, Jonathan Shapey, Tom Vercauteren, Kazumi Chia, Omar Al-Salihi, Justin Leu, Lia Halasz, Yury Velichko, Chunhao Wang, John Kirkpatrick, Scott Floyd, Zachary J. Reitman, Trey Mullikin, Ulas Bagci, Sean Sachdev, Jona A. Hattangadi-Gluth, Tyler Seibert, Nikdokht Farid, Connor Puett, Matthew W. Pease, Kevin Shiue, Syed Muhammad Anwar, Shahriar Faghani, Muhammad Ammar Haider, Pranav Warman, Jake Albrecht, András Jakab, Mana Moassefi, Verena Chung, Alejandro Aristizabal, Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah Sheller, Christina Huang, Aaron Coley, Siddharth Ghanta, Alex Schneider, Conrad Sharp, Rachit Saluja, Florian Kofler, Philipp Lohmann, Phillipp Vollmuth, Louis Gagnon, Maruf Adewole, Hongwei Bran Li, Anahita Fathi Kazerooni, Nourel Hoda Tahon, Udunna Anazodo, Ahmed W. Moawad, Bjoern Menze, Marius George Linguraru, Mariam Aboian, Benedikt Wiestler, Ujjwal Baid, Gian-Marco Conte, Andreas M. Rauschecker, Ayman Nada, Aly H. Abayazeed, Raymond Huang, Maria Correia de Verdier, Jeffrey D. Rudie, Spyridon Bakas, Evan Calabrese
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge, a competition aimed at developing more accurate automated segmentation algorithms for brain MRIs with expert-annotated target labels. The dataset includes 3D post-contrast T1-weighted radiotherapy planning MRIs from multiple institutions, along with single-label “target volumes” representing the gross tumor volume (GTV). The challenge focuses on meningiomas, including preoperative and postoperative cases, and requires teams to develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using adapted lesion-wise Dice Similarity Coefficient and 95% Hausdorff distance metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a big challenge for machines learning experts to help improve brain tumor treatment. They have a huge set of brain MRI scans with special labels that show where the tumors are. The goal is to create computer models that can accurately identify these tumors, which will help doctors give patients better treatment and better outcomes. |
