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[Preprint]. 2024 Nov 24:arXiv:2305.09011v6.

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

Hongwei Bran Li^{1

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3}, Gian Marco Conte⁴, Qingqiao Hu^{1

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45}, Syed Muhammad Anwar^{12

13}, Florian Kofler^{5

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7}, Ivan Ezhov², Koen van Leemput⁹, Marie Piraud⁵, Maria Diaz²⁴, Byrone Cole²⁴, Evan Calabrese^{27

43}, Jeff Rudie⁴³, Felix Meissen², Maruf Adewole¹¹, Anastasia Janas¹⁷, Anahita Fathi Kazerooni^{18

14}, Dominic LaBella¹⁹, Ahmed W Moawad²², Keyvan Farahani²³, James Eddy²⁴, Timothy Bergquist²⁴, Verena Chung²⁴, Russell Takeshi Shinohara^{14

25}, Farouk Dako²⁶, Walter Wiggins²⁷, Zachary Reitman¹⁹, Chunhao Wang¹⁹, Xinyang Liu^{12

13}, Zhifan Jiang^{12

13}, Ariana Familiar¹⁸, Elaine Johanson³⁰, Zeke Meier³¹, Christos Davatzikos^{14

15}, John Freymann^{32

23}, Justin Kirby^{32

23}, Michel Bilello^{14

15}, Hassan M Fathallah-Shaykh³³, Roland Wiest^{34

35}, Jan Kirschke²¹, Rivka R Colen^{36

37}, Aikaterini Kotrotsou³⁷, Pamela Lamontagne³⁸, Daniel Marcus^{39

40}, Mikhail Milchenko^{39

40}, Arash Nazeri⁴⁰, Marc-André Weber⁴¹, Abhishek Mahajan⁴², Suyash Mohan^{14

15}, John Mongan⁴³, Christopher Hess⁴³, Soonmee Cha⁴³, Javier Villanueva-Meyer⁴³, Errol Colak⁴⁴, Priscila Crivellaro⁴⁴, Andras Jakab⁴⁵, Jake Albrecht²⁴, Udunna Anazodo²⁹, Mariam Aboian¹⁷, Thomas Yu¹⁰, Verena Chung²⁴, Timothy Bergquist²⁴, James Eddy²⁴, Jake Albrecht²⁴, Ujjwal Baid^{14

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16}, Spyridon Bakas^{14

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16}, Marius George Linguraru⁸, Bjoern Menze¹, Juan Eugenio Iglesias¹⁰, Benedikt Wiestler³

Affiliations

¹ University of Zurich, Switzerland.
² Department of Informatics, Technical University Munich, Germany.
³ Klinikum rechts der Isar, Technical University of Munich, Germany.
⁴ Mayo Clinic, Rochester, USA.
⁵ Helmholtz AI, Helmholtz Munich, Germany.
⁶ TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany.
⁷ Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany.
⁸ Children's National Hospital, Washington DC, USA and George Washington University, USA.
⁹ Finnish Center for Artificial Intelligence, Finland.
¹⁰ Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, USA.
¹¹ Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria.
¹² Children's National Hospital, Washington DC, USA.
¹³ George Washington University, Washington DC, USA.
¹⁴ Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
¹⁵ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁶ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁷ Yale University, New Haven, CT, USA.
¹⁸ Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
¹⁹ Duke University Medical Center, Department of Radiation Oncology, USA.
²⁰ Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
²¹ Department of Neuroradiology, Technical University of Munich, Munich, Germany.
²² Mercy Catholic Medical Center, Darby, PA, USA.
²³ Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA.
²⁴ Sage Bionetworks, USA.
²⁵ Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA.
²⁶ Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
²⁷ Duke University Medical Center, Department of Radiology, USA.
²⁸ Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
²⁹ Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada.
³⁰ PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA.
³¹ Booz Allen Hamilton, McLean, VA, USA.
³² Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA.
³³ Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, USA.
³⁴ Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
³⁵ Support Centre for Advanced Neuroimaging Inselspital, Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Bern, Switzerland.
³⁶ University of Pittsburgh Medical Center, PA, USA.
³⁷ Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
³⁸ Department of Psychology, Washington University, St. Louis, MO, USA.
³⁹ Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA.
⁴⁰ Department of Radiology, Washington University, St. Louis, MO, USA.
⁴¹ Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057 Rostock, Germany.
⁴² Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India.
⁴³ University of California San Francisco, CA, USA.
⁴⁴ University of Toronto, Toronto, ON, Canada.
⁴⁵ University of Debrecen, Hungary Stony Brook University, NY, USA.

PMID: 37608932
PMCID: PMC10441440

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

Hongwei Bran Li et al. ArXiv. 2024.

[Preprint]. 2024 Nov 24:arXiv:2305.09011v6.

Authors

Affiliations

¹ University of Zurich, Switzerland.
² Department of Informatics, Technical University Munich, Germany.
³ Klinikum rechts der Isar, Technical University of Munich, Germany.
⁴ Mayo Clinic, Rochester, USA.
⁵ Helmholtz AI, Helmholtz Munich, Germany.
⁶ TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany.
⁷ Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany.
⁸ Children's National Hospital, Washington DC, USA and George Washington University, USA.
⁹ Finnish Center for Artificial Intelligence, Finland.
¹⁰ Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, USA.
¹¹ Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria.
¹² Children's National Hospital, Washington DC, USA.
¹³ George Washington University, Washington DC, USA.
¹⁴ Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
¹⁵ Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁶ Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹⁷ Yale University, New Haven, CT, USA.
¹⁸ Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
¹⁹ Duke University Medical Center, Department of Radiation Oncology, USA.
²⁰ Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
²¹ Department of Neuroradiology, Technical University of Munich, Munich, Germany.
²² Mercy Catholic Medical Center, Darby, PA, USA.
²³ Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA.
²⁴ Sage Bionetworks, USA.
²⁵ Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA.
²⁶ Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
²⁷ Duke University Medical Center, Department of Radiology, USA.
²⁸ Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
²⁹ Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada.
³⁰ PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA.
³¹ Booz Allen Hamilton, McLean, VA, USA.
³² Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA.
³³ Department of Neurology, The University of Alabama at Birmingham, Birmingham, AL, USA.
³⁴ Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.
³⁵ Support Centre for Advanced Neuroimaging Inselspital, Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Bern, Switzerland.
³⁶ University of Pittsburgh Medical Center, PA, USA.
³⁷ Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
³⁸ Department of Psychology, Washington University, St. Louis, MO, USA.
³⁹ Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA.
⁴⁰ Department of Radiology, Washington University, St. Louis, MO, USA.
⁴¹ Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057 Rostock, Germany.
⁴² Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India.
⁴³ University of California San Francisco, CA, USA.
⁴⁴ University of Toronto, Toronto, ON, Canada.
⁴⁵ University of Debrecen, Hungary Stony Brook University, NY, USA.

PMID: 37608932
PMCID: PMC10441440

Abstract

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.

Keywords: BraTS; MRI; brain; challenge; image synthesis; machine learning; segmentation; tumor.

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Figures

**Fig.1:**
The details of training, validation, and test sets. During the validation and test stages, for each subject, the segmentation mask corresponding to images is not available, and one of the four modalities will be **randomly** excluded (‘dropout’). The participants are required to synthesize any missing modalities in the test stage.

See this image and copyright information in PMC

References

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This is a preprint.

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

Affiliations

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

Authors

Affiliations

Abstract

Figures

References

Publication types

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