Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Maria Correia de Verdier, Rachit Saluja, Louis 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 Simon, Nico Sollmann, David 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 Altes, Patil Basavasagar, Martin Bendszus, Gianluca Brugnara, Jaeyoung Cho, Yaseen Dhemesh, Brandon K. K. Fields, Filip Garrett, Jaime Gass, Lubomir Hadjiiski, Jona Hattangadi-Gluth, Christopher Hess, Jessica L. Houk, Edvin Isufi, Lester J. Layfield, George Mastorakos, John Mongan, Pierre Nedelec, Uyen Nguyen, Sebastian Oliva, Matthew W. Pease, Aditya Rastogi, Jason Sinclair, Robert X. Smith, Leo P. Sugrue, Jonathan Thacker, Igor Vidic, Javier Villanueva-Meyer, Nathan S. White, Mariam Aboian, Gian Marco Conte, Anders Dale, Mert R. Sabuncu, Tyler M. Seibert, Brent Weinberg, Aly Abayazeed, Raymond Huang, Sevcan Turk, Andreas M. Rauschecker, Nikdokht Farid, Philipp Vollmuth, Ayman Nada, Spyridon Bakas, Evan Calabrese, Jeffrey D. Rudie

TL;DR

The paper introduces the BraTS 2024 post-treatment glioma segmentation challenge, addressing the need for reliable automated segmentation in post-treatment MRI with a new RC class. It compiles a large, multi-institutional dataset (~2,200 cases) and a BraTS-like preprocessing/annotation pipeline, plus lesion-wise evaluation metrics (DSC and HD95) and a rank-based scoring scheme. The study highlights the clinical relevance of distinguishing residual disease from treatment-related changes and sets the stage for future work on longitudinal and multimodal data. Overall, it provides a publicly accessible benchmark to advance automated residual-tumor volume assessment and inform post-treatment management in diffuse gliomas.

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.

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

TL;DR

The paper introduces the BraTS 2024 post-treatment glioma segmentation challenge, addressing the need for reliable automated segmentation in post-treatment MRI with a new RC class. It compiles a large, multi-institutional dataset (~2,200 cases) and a BraTS-like preprocessing/annotation pipeline, plus lesion-wise evaluation metrics (DSC and HD95) and a rank-based scoring scheme. The study highlights the clinical relevance of distinguishing residual disease from treatment-related changes and sets the stage for future work on longitudinal and multimodal data. Overall, it provides a publicly accessible benchmark to advance automated residual-tumor volume assessment and inform post-treatment management in diffuse gliomas.

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.
Paper Structure (8 sections, 4 figures, 1 table)

This paper contains 8 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Data
  4. Data Preprocessing
  5. Tumor Annotation Protocol
  6. Performance Evaluation
  7. Discussion
  8. Conclusion

Figures (4)

  • Figure 1: Map showing institutions from USA and Germany contributing data to the 2024 Brain Tumor Segmentation (BraTS) post-treatment glioma challenge and their relative sample size.
  • Figure 2: Data Processing and Annotation Workflow for creating the 2024 BraTS post-treatment glioma challenge dataset.
  • Figure 3: Tumor sub-regions considered in the 2024 BraTS post-treatment glioma challenge. Image panels with the tumor sub-regions annotated in the different mpMRI scans and combined segmentations on mpMRI. The enhancing tissue (blue) visible on a T1-Gd scan, the non-enhancing tumor core (red) visible on a T1-Gd scan, the surrounding non-enhancing FLAIR hyperintensity (green) visible on a FLAIR scan and the resection cavity (yellow) visible on a T2 scan. The combined segmentations generating the final tumor sub-region labels visible on mpMRI, as provided to the challenge participants: enhancing tissue (blue), the surrounding non-enhancing FLAIR hyperintensity (green), the non-enhancing tumor core (red), and the resection cavity (yellow).
  • Figure 4: Common errors in automated segmentations in the 2024 BraTS post-treatment glioma challenge. The top row shows typical segmentation errors, and the bottom row shows manually corrected labels. Color codes: blue for enhancing tissue (ET), red for non-enhancing tumor core (NETC), green for surrounding non-enhancing FLAIR hyperintensity (SNFH), and yellow for resection cavity (RC). Additional Detailed examples are available in the https://drive.google.com/file/d/10oI-KUxQVT0FpClOYZVu_zad1yi4WQC6/view.