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数字病理学图像的最低分辨率要求,以准确分类

Lydia Neary-Zajiczek¹, Linas Beresna², Benjamin Razavi³

¹Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.

Medical image analysis

|August 3, 2023

概括

数字病理学可以解决人员短缺问题. 这项研究发现,较低的图像分辨率足以进行乳腺组织分类,从而使病理学分类的低成本系统成为可能.

科学领域:

数字病理学数字病理学
计算病理学计算病理学
组织病理学图像分析分析.

背景情况:

病理学部门面临人员危机,阻碍及时诊断.
数字病理学的采用受到成本和对图像质量的担忧的限制.
对于组织病理学的定量图像质量要求还没有得到很好的定义.

研究的目的:

量化确定乳腺组织病理学二元分类的最小图像质量要求.
评估空间和采样分辨率对诊断准确性的影响.
评估用于病理学分类的低成本成像系统的可行性.

主要方法:

在公开的基因病理学数据集上训练有素的深度学习分类器.
通过计算降解图像来模拟降低的空间和采样分辨率.
通过使用理论模型,在各种分辨率级别评估诊断准确性.

主要成果:

显著的图像退化是可能的,同时保持可接受的诊断准确度.
宏观特征足以对染色乳腺组织进行二元分类.
最低分辨率要求远低于当前的全幻灯片成像系统.

结论:

数字病理学图像质量要求可以量化定义.
低成本,低分辨率的成像系统可以支持病理学分类.
这种方法可以减轻劳动力压力,提高诊断效率.

关键词:

自动诊断的自动化诊断.深度学习是一种深度学习.数字病理学数字病理学图像质量图像质量

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