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 (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 affect 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缺失数据问题。通常，自动化MRI扫描分割基于多种扫描模态（如T1加权、T2加权、T1CE、FLAIR）。然而，扫描图像常常存在模糊、缺失或无法使用的情况。我们探究了能否合成缺失扫描的问题，并通过从给定的T1加权扫描合成T2加权扫描，证明了这在原则上是可行的。我们的首要目标是计算一幅与缺失扫描高度接近的图像，并以平均均方误差（MSE）作为衡量指标。为此，我们开发/采用了多种方法，包括随机基线方法、基于聚类的方法以及基于条件GAN的像素到像素翻译方法（Pix2Pix）。最低MSE由我们提出的基于聚类的方法实现。第二个目标是比较使用合成扫描对分割过程的影响。为此，我们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，评估肿瘤识别的分割效果。评估结果表明，在许多情况下，使用合成扫描（尤其是通过Pix2Pix方法）的分割效果良好。