Cover Image

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Hongwei Bran, Conte, Gian Marco, Hu, Qingqiao, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M., Wiest, Roland, Kirschke, Jan, Colen, Rivka R., Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, Wiestler, Benedikt
Format:	Preprint
Published:	2023
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866910710935584768
author	Li, Hongwei Bran Conte, Gian Marco Hu, Qingqiao Anwar, Syed Muhammad Kofler, Florian Ezhov, Ivan van Leemput, Koen Piraud, Marie Diaz, Maria Cole, Byrone Calabrese, Evan Rudie, Jeff Meissen, Felix Adewole, Maruf Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Moawad, Ahmed W. Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Johanson, Elaine Meier, Zeke Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva, Javier Colak, Meyer Errol Crivellaro, Priscila Jakab, Andras Albrecht, Jake Anazodo, Udunna Aboian, Mariam Yu, Thomas Chung, Verena Bergquist, Timothy Eddy, James Albrecht, Jake Baid, Ujjwal Bakas, Spyridon Linguraru, Marius George Menze, Bjoern Iglesias, Juan Eugenio Wiestler, Benedikt
author_facet	Li, Hongwei Bran Conte, Gian Marco Hu, Qingqiao Anwar, Syed Muhammad Kofler, Florian Ezhov, Ivan van Leemput, Koen Piraud, Marie Diaz, Maria Cole, Byrone Calabrese, Evan Rudie, Jeff Meissen, Felix Adewole, Maruf Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Moawad, Ahmed W. Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Johanson, Elaine Meier, Zeke Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva, Javier Colak, Meyer Errol Crivellaro, Priscila Jakab, Andras Albrecht, Jake Anazodo, Udunna Aboian, Mariam Yu, Thomas Chung, Verena Bergquist, Timothy Eddy, James Albrecht, Jake Baid, Ujjwal Bakas, Spyridon Linguraru, Marius George Menze, Bjoern Iglesias, Juan Eugenio Wiestler, Benedikt
contents	Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.
format	Preprint
id	arxiv_https___arxiv_org_abs_2305_09011
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) Li, Hongwei Bran Conte, Gian Marco Hu, Qingqiao Anwar, Syed Muhammad Kofler, Florian Ezhov, Ivan van Leemput, Koen Piraud, Marie Diaz, Maria Cole, Byrone Calabrese, Evan Rudie, Jeff Meissen, Felix Adewole, Maruf Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Moawad, Ahmed W. Farahani, Keyvan Eddy, James Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Familiar, Ariana Johanson, Elaine Meier, Zeke Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva, Javier Colak, Meyer Errol Crivellaro, Priscila Jakab, Andras Albrecht, Jake Anazodo, Udunna Aboian, Mariam Yu, Thomas Chung, Verena Bergquist, Timothy Eddy, James Albrecht, Jake Baid, Ujjwal Bakas, Spyridon Linguraru, Marius George Menze, Bjoern Iglesias, Juan Eugenio Wiestler, Benedikt Image and Video Processing Computer Vision and Pattern Recognition Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.
title	The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2305.09011