Automated brain tumor segmentation methods are well established, reaching performance levels with clear clinical utility. Most algorithms require four input magnetic resonance imaging (MRI) modalities, typically T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some of these sequences are often missing in clinical practice, e.g., because of time constraints and/or image artifacts (such as patient motion). Therefore, substituting missing modalities to recover segmentation performance in these scenarios is highly desirable and necessary for the more widespread adoption of such algorithms in clinical routine. In this work, we report the set-up of the Brain MR Image Synthesis Benchmark (BraSyn), organized in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The objective of the challenge is to benchmark image synthesis methods that realistically synthesize missing MRI modalities given multiple available images to facilitate automated brain tumor segmentation pipelines. The image dataset is multi-modal and diverse, created in collaboration with various hospitals and research institutions.

翻译：自动化脑肿瘤分割方法已发展成熟，其性能水平具有明确的临床实用性。大多数算法需要四种输入磁共振成像（MRI）模态，通常包括平扫和增强T1加权图像、T2加权图像以及FLAIR图像。然而，在临床实践中，部分序列常常缺失，例如因时间限制和/或图像伪影（如患者运动）所致。因此，在这些场景下通过替代缺失模态来恢复分割性能，对于此类算法在临床常规中的更广泛应用而言，极具必要性且至关重要。本文报告了脑磁共振图像合成基准挑战（BraSyn）的设置，该挑战与2023年医学图像计算与计算机辅助介入（MICCAI）大会同期举办。该挑战的目标是基准测试图像合成方法，这些方法能够在给定多个可用图像的基础上，真实地合成缺失的MRI模态，从而促进自动化脑肿瘤分割流水线的发展。该图像数据集为多模态且多样化的，由多家医院和研究机构合作创建。