  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.
