MSIreg:一个R包,用于无监督的质谱和H&E图像的共同注册
Sai Srikanth Lakkimsetty1, Andreas Weber2,3, Kylie A Bemis1
1Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States.
Bioinformatics (Oxford, England)
|October 17, 2024
概括
精确的质谱图像 (MS图像) 和H&E显微镜图像的共同记录对于组织诊断至关重要. 新的MSIreg R包为精确的图像对齐提供了一个没有里程碑的解决方案,改善了联合解释.
科学领域:
- 计算病理学计算病理学
- 生物信息学是一种生物信息学.
- 医学成像分析分析 医学成像分析
背景情况:
- 质谱学 (MS) 和血素和素 (H&E) 染色组织图像的联合分析有助于病理诊断.
- 图像比例,空间分辨率和图像通道的显著差异,以及空间变形,阻碍了图像的准确解释.
- 精确的联合注册对于多模态组织成像数据的有效联合分析至关重要.
研究的目的:
- 引入MSIreg,这是一个开源的R包,旨在准确地共同注册MS图像和H&E染色组织图像.
- 为高维的MS成像实验提供强大的解决方案,每个空间位置有数千个质量特征.
- 为了实现图像正统注册的无里程碑工作流程,解决成像模式之间的空间变化所带来的挑战.
主要方法:
- 开发MSIreg,一个R包,实现一个没有里程碑的共同注册工作流程.
- 在MSIreg包中整合用于绩效评估的定量指标.
- 使用一个开源的R包,用于方法和案例研究.
主要成果:
- MSIreg促进了MSI和H&E图像的准确联合注册,克服了不同空间分辨率和变形的挑战.
- 该套件在复杂的场景中表现出有效性,包括对具有大变形的连续组织进行联合注册.
- 29个组织微阵列核心的同时成功联合注册突显了MSIreg方法的可扩展性和稳定性.
结论:
- 对MSI和H&E图像的准确联合注册是它们在病理学中的联合解释的关键先决条件.
- 该MSIreg R套件提供了一个有效的,开源的,没有里程碑的工具,用于共同注册多模式组织图像.
- MSIreg 增强了组织形态的表征,并通过综合成像分析支持改善诊断能力.
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