腹部医学图像注册的注释多模态真值数据集
基于XCAT幻影和CycleGAN生成的多模态腹部医学图像数据集，包含T1-MRI、CT、CBCT，可用于图像分割和注册算法的真值验证。
基本信息
资源简介
该数据集由4D扩展心脏-躯干（XCAT）幻影和真实患者数据通过CycleGAN网络架构生成，包含T1加权磁共振成像（MRI）、计算机断层扫描（CT）和锥束CT（CBCT）图像，这些图像在生成时已内在对齐，可作为图像分割和注册的真值。数据集主要应用于优化多模态医学图像注册算法的参数，尤其是肝脏注册。
下载信息
注册下载
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暂未开放公开下载
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免登录有偿下载
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暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集说明
腹部医学图像注册的注释多模态真值数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2012.01582v2
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract: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. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. 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 (MRI), computed tomography (CT), and cone beam CT (CBCT) images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registrat
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