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在MRI图像中的腰椎细分:一个数据集和一个公共基准

Jasper W van der Graaf^1,2, Miranda L van Hooff^3,4, Constantinus F M Buckens⁵

¹Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. jasper.vandergraaf@radboudumc.nl.

Scientific data

|March 2, 2024

概括

这项研究引入了一个大型的公共腰椎MRI数据集,对脊椎,圆盘和脊柱通道进行细分. 本资源旨在推进脊柱细分研究,并增强腰部疼痛的诊断能力.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
计算机视觉计算机视觉

背景情况:

腰部疼痛是一种普遍的疾病,通常使用腰椎MRI诊断.
在腰椎MRI中精确细分解剖结构对于诊断至关重要,但具有挑战性.
大型注释多中心数据集的有限可用性阻碍了强大的细分算法的开发.

研究的目的:

呈现一个新的,大规模的,多中心的腰椎脊椎MRI数据集,具有全面的参考细分.
在本数据集上为自动化细分算法建立基线性能指标.
为了促进 benchmarking 和推进脊柱成像分析的持续细分挑战.

主要方法:

策划了447名来自4家医院的218名腰部疼痛患者的T1和T2斜腰MRI系列数据集.
采用了一种代的半自动注释方法,将算法生成的细分与专家手动校正相结合.
评估了使用隔离测试集的细分性能,并将基线算法与nnU-Net.Net进行了比较.

主要成果:

数据集包括脊椎,脊椎间盘 (IVD) 和脊柱通道的详细细分.
基线算法和nnU-Net在参考细分上表现相似.
确立的参考性能值和算法评估的持续挑战.

结论:

本文所介绍的数据集是推动腰椎自动化MRI细分的宝贵资源.
这些发现为当前的细分技术提供了基准,并鼓励进一步的研究.
预计这一举措将促进合作,并提高腰椎MRI的诊断效用.

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