This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize a missing magnetic resonance image sequence, given other available sequences, to facilitate tumor segmentation pipelines trained on complete sets of image sequences. This problem can be tackled using deep learning within the framework of paired image-to-image translation. In this study, we propose investigating the effectiveness of a commonly used deep learning framework, such as Pix2Pix, trained under the supervision of different image-quality loss functions. Our results indicate that the use of different loss functions significantly affects the synthesis quality. We systematically study the impact of various loss functions in the multi-sequence MR image synthesis setting of the BraSyn challenge. Furthermore, we demonstrate how image synthesis performance can be optimized by combining different learning objectives beneficially.

翻译：本工作针对2023年脑肿瘤分割（BraTS）挑战中的脑磁共振图像合成（BraSyn）任务展开研究。该挑战邀请研究者利用现有序列合成缺失的磁共振图像序列，以促进基于完整图像序列训练的肿瘤分割流程。该问题可通过配对图像到图像翻译框架下的深度学习技术解决。本研究旨在探究Pix2Pix等常用深度学习框架在不同图像质量损失函数监督下的有效性。结果表明，不同损失函数的使用会显著影响合成质量。我们系统分析了BraSyn挑战中多序列MR图像合成场景下各类损失函数的影响，并进一步展示了如何通过有益组合不同学习目标来优化图像合成性能。