Multimodal ground truth datasets for abdominal medical image registration [data]

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac–torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered.

标注数据的稀疏性是配准等医学图像处理任务的主要限制因素。配准后的多模态医学图像数据对于疾病诊断与介入医疗操作的顺利开展至关重要。为解决数据短缺问题，本文提出一种可生成带标注多模态四维（4D）数据集的方法。我们采用循环生成对抗网络（CycleGAN）架构，从四维扩展心脏躯干体模（XCAT phantom）与真实患者数据中生成多模态合成医学数据。XCAT体模可提供器官掩码，因此生成的数据集可作为图像分割与配准任务的真值（ground truth）基准。与真实患者数据相比，该合成数据在图像体素强度分布与噪声特性方面均表现出良好的一致性。所生成的T1加权磁共振成像（T1-weighted magnetic resonance imaging）、计算机断层扫描（CT）与锥形束CT图像本身固有具备共配准属性。