Li, Hongwei Bran; Conte, Gian Marco; 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; ... (28 June 2023). The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn). (arXiv). Cornell University
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2305.09011v5.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (399kB) | Preview |
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.
Item Type: |
Working Paper |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology |
UniBE Contributor: |
Wiest, Roland Gerhard Rudi |
Subjects: |
600 Technology > 610 Medicine & health |
Series: |
arXiv |
Publisher: |
Cornell University |
Language: |
English |
Submitter: |
Martin Zbinden |
Date Deposited: |
29 Apr 2024 14:45 |
Last Modified: |
29 Apr 2024 14:54 |
PubMed ID: |
37608932 |
ArXiv ID: |
2305.09011v5 |
Uncontrolled Keywords: |
BraTS MRI brain challenge image synthesis machine learning segmentation tumor |
BORIS DOI: |
10.48350/196332 |
URI: |
https://boris.unibe.ch/id/eprint/196332 |
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