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Confocal Fluorescence Microscopy

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...

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Updated: Jun 20, 2025

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

通过机器学习,在超光谱成像中推进激光除评估.

Viacheslav V Danilov¹, Martina De Landro¹, Eric Felli²

¹Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.

Computers in biology and medicine

|July 17, 2024

概括

超光谱成像 (HSI) 分析用于激光除瘤切除使用了一个新的工作流程. 这种方法结合了PCA,t-SNE和Faster R-CNN,用于精确的剥离检测和细分.

关键词:

集群集成是指集群集成.缩小尺寸的缩小方式超光谱成像技术的使用.对象检测检测对象检测对象检测分段化分段化分段化分段化组织的剥离是组织的剥离.

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Last Updated: Jun 20, 2025

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科学领域:

医学成像医学成像
计算病理学计算病理学
手术瘤学手术瘤学

背景情况:

超光谱成像 (HSI) 在医学上越来越重要,特别是在微创性瘤切除中激光切除治疗的术内评估中.
HSI数据的高维度和复杂性需要专门的端到端图像处理工作流程来进行有效的分析.

研究的目的:

提出和评估一个多阶段的工作流程,用于高光谱数据分析,以检测和细分激光切除区域.
调查不同组件,模式和缩小维度技术对除检测性能的影响.

主要方法:

实现了主要组件分析 (PCA) 和t分布式静态邻居嵌入 (t-SNE) 以减少维度.
利用基于快速区域的卷积神经网络 (快速R-CNN) 来准确地定位切除区域.
采用了平均转移算法来进行高质量的,无监督的废弃区域细分.

主要成果:

综合工作流证明了尺寸缩小技术和数据模式对除检测准确性的显著影响.
在一个独立的测试组上检测出了0.74的平均精度,这表明了很强的概括性.
平均转移算法提供了高质量的细分,而不需要手动定义集群.

结论:

开发的多阶段工作流有效地分析复杂的超频谱数据用于手术内评估.
集成PCA,t-SNE和更快的R-CNN可以提高HSI数据的解释,从而实现可靠的切除检测和细分系统.
这种方法有望改善最小侵入性瘤切除结果.