了解当前CT扫描重建中三维深度学习的最新方法对研究人员和从业人员都至关重要。

2023年发表在Tomography的文章通过广泛的文献调查，为研究人员、临床医生以及对深度学习与医学影像交叉领域感兴趣的任何人提供了宝贵的资源。

Lotus将会为各位读者带来含有Lotus查阅了大量资料的，

关于这篇CT重建算法的综述的系列文献解读：

#098 有趣的深度学习基础

#099 如何进行3D深度学习相关文献的检索

#100 60篇深度学习重建算法文章总结

#104 CT重建算法基础讲解

#105 迭代重建算法

#107 深度学习图像重建算法基础概念解析

#108 3D深度学习图像重建算法

今天我们来分析在CT重建中可用的公开数据集以及相关CT深度学习图像重建算法。

Part1重要的数据集

深度学习(deep learning，DL)是人工智能的一个分支，因为它需要大量的训练数据，是一种依赖数据的方法。

因而，当深度学习应用于医学图像分析时，缺乏训练数据就成了一大挑战和瓶颈。

由于隐私和伦理限制，医疗数据属于医疗机构所有，无法轻易公开。

当研究人员在他们的私人数据上评估和报告他们的算法的时候，往往很难对这些研究进行横向比较和评价。

为了解决数据上的一些问题，MICCAI，ISBI，AAPM以及其他会议和学术机构发起了许多与DL相关的医学图像分析竞赛。

如上面的图所示，2013年到2020年涌现了大量的不同类型的数据集。

截止至2020年的统计来看，全球公开的CT方面的数据集有124个，占全体医学相关的数据集的24%。

Part2CT DL算法汇总

把#100当中的60篇深度学习重建算法文章按照时间-数据集进行整理可以关注到重建算法比较“倚仗”的数据库有这么几个：

• LUNA 16
• LIDC-IDRI
• 2016 NIH-AAPM-Mayo
• MSCT

49篇相关研究见下表所示，文献编号与文末文献对应。

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