With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n=77) and transitional vertebrae (n=161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.

翻译：随着深层学习算法的出现,完全自动化的放射图像分析就近了。在脊柱成像中,提出了若干基于图集和形状的图集以及深层的学习分化算法,以便随后对形态学和病理学进行自动分析。第一个大规模紫外线分解挑战(VerSe 2019)显示,这些在正常解剖学上表现良好,但在培训数据集中并不经常出现的变异中却出故障。基于这一经验,我们报告了2020年VerSe数据集的大幅增长以及VerSe挑战第二次迭代(MICCAI 2020,秘鲁利马)的结果。 VerSe 2020包括一个注解的脊椎计算图象(CT),来自300个主题,有4142个完全可视化和附加注释的脊椎。从四个不同的扫描器制造商收集到的多个中心,这些图解变,丰富了诸如例异常(n=77)和过渡性脊椎(n=161)等案例。元数包括了脊椎标签信息、以人类机器精准混合算法和演算法升级法升级法的发展。