We consider a missing data problem in the context of automatic segmentation methods for Magnetic Resonance Imaging (MRI) brain scans. Usually, automated MRI scan segmentation is based on multiple scans (e.g., T1-weighted, T2-weighted, T1CE, FLAIR). However, quite often a scan is blurry, missing or otherwise unusable. We investigate the question whether a missing scan can be synthesized. We exemplify that this is in principle possible by synthesizing a T2-weighted scan from a given T1-weighted scan. Our first aim is to compute a picture that resembles the missing scan closely, measured by average mean squared error (MSE). We develop/use several methods for this, including a random baseline approach, a clustering-based method and pixel-to-pixel translation method by Isola et al. (Pix2Pix) which is based on conditional GANs. The lowest MSE is achieved by our clustering-based method. Our second aim is to compare the methods with respect to the effect that using the synthesized scan has on the segmentation process. For this, we use a DeepMedic model trained with the four input scan modalities named above. We replace the T2-weighted scan by the synthesized picture and evaluate the segmentations with respect to the tumor identification, using Dice scores as numerical evaluation. The evaluation shows that the segmentation works well with synthesized scans (in particular, with Pix2Pix methods) in many cases.

翻译：我们研究了磁共振成像（MRI）脑部扫描自动分割方法中的数据缺失问题。通常，自动化MRI扫描分割依赖于多模态扫描（如T1加权、T2加权、T1CE、FLAIR）。然而，实际应用中常出现扫描图像模糊、缺失或无法使用的情况。我们探究了缺失扫描图像能否被合成这一问题，并通过从给定的T1加权扫描合成T2加权扫描的实例，证明了该方法的可行性。首要目标是生成与缺失扫描高度相似的图像，以平均均方误差（MSE）为评估标准。我们开发/采用了多种方法进行合成，包括随机基线方法、基于聚类的方法以及Isola等人提出的基于条件GAN的像素到像素转换方法（Pix2Pix）。其中，聚类方法取得了最低的MSE。第二个目标是比较使用合成扫描图像对分割过程的影响。为此，我们采用使用上述四种输入扫描模态训练的DeepMedic模型，将T2加权扫描替换为合成图像，并以Dice分数作为数值评估指标，评估分割结果对肿瘤识别的效果。评估表明，在许多情况下，基于合成扫描（特别是Pix2Pix方法）的分割效果良好。