SKM-TEA

The SKM-TEA dataset consists of imaging data and annotations for 155 quantitative double echo steady state MRI knee scans acquired clinically at Stanford. The data includes the raw kspace, DICOM images, segmentations of six tissues, and bounding boxes for 16 pathologies. The dataset consists of 86 scans for training, 33 scans for validation, and 36 scans for testing. All data were acquired with 2x1 parallel imaging using 8 or 15 coils with elliptical MRI sampling. Missing data was subsequently estimated using ARC (GE) parallel imaging with the GE Orchestra MATLAB SDK. This data is considered the fully-sampled kspace. DICOM images were manually segmented for articular tissue and the meniscus. They were also annotated for 16 different pathologies that were extracted and localized (3D bounding boxes) based on corresponding radiology reports. More details here:https://openreview.net/forum?id=YDMFgD_qJuA

SKM-TEA数据集包含155例在斯坦福大学临床采集的定量双回波稳态MRI膝关节扫描的成像数据及标注信息。数据涵盖原始k空间数据、DICOM图像、六种组织的分割结果以及16种病变的边界框。该数据集由86例训练扫描、33例验证扫描和36例测试扫描组成。所有数据均采用8或15个线圈的2×1并行成像技术，并结合椭圆MRI采样方式采集。缺失数据随后通过GE Orchestra MATLAB SDK中的ARC（GE）并行成像技术进行估计。该数据被视为全采样k空间数据。研究人员对DICOM图像中的关节组织和半月板进行了手动分割。此外，基于相应的放射学报告，研究人员对16种不同病变进行了标注、提取和定位（3D边界框）。更多细节请参见：https://openreview.net/forum?id=YDMFgD_qJuA