论文标题
诗：多探测器CT图像的椎骨标记和分割基准
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
论文作者
论文摘要
椎骨标记和分割是自动化脊柱处理管道中的两个基本任务。预计脊柱图像的可靠，准确处理将使临床决策支持系统有益于诊断，手术计划以及基于人群的脊柱和骨骼健康的分析。但是，设计自动化算法的脊柱加工算法主要是由于解剖结构和采集方案的巨大变化，并且由于严重的公开数据短缺。在解决这些局限性方面，大规模的椎骨细分挑战（Verse）与2019年和2020年国际医学图像计算和计算机辅助干预（MICCAI）的国际会议一起组织，并呼吁采用算法来标记和椎骨的标签和细分。制备了355例患者的两个数据集，其中包含374个多探测器CT扫描，并通过人类 - 摩金杂种算法（https：///osf.io/nqjyw/，https：httpps:/httpps:/ httpps://osf.io oosf.io osf.io osf.io osf.io osf.io/t98fz/）在Voxel-Level上单独注释4505个椎骨。在这些数据集上，总共对25种算法进行了基准测试。在这项工作中，我们介绍了该评估的结果，并进一步研究了椎骨级，扫描级别和不同视野的性能变化。我们还通过评估来自另一种迭代的数据的一个挑战迭代的最高表现算法来评估数据隐域转移的方法的普遍性。经文的主要外卖：算法在标记和分割脊柱扫描中的性能取决于其在罕见的解剖学变化的情况下正确识别椎骨的能力。有关经文的内容和代码可以通过以下网址访问：https：//github.com/anjany/verse。
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The content and code concerning VerSe can be accessed at: https://github.com/anjany/verse.
