Lekadir Karim, Elson Daniel S, Requejo-Isidro Jose, Dunsby Christopher, McGinty James, Galletly Neil, Stamp Gordon, French Paul M W, Yang Guang-Zhong
Visual Information Processing Group, Department of Computing, Imperial College London, United Kingdom.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):586-93. doi: 10.1007/11866763_72.
Multidimensional fluorescence imaging is a powerful molecular imaging modality that is emerging as an important tool in the study of biological tissues. Due to the large volume of multi-spectral data associated with the technique, it is often difficult to find the best combination of parameters to maximize the contrast between different tissue types. This paper presents a novel framework for the characterization of tissue compositions based on the use of time resolved fluorescence imaging without the explicit modeling of the decays. The composition is characterized through soft clustering based on manifold embedding for reducing the dimensionality of the datasets and obtaining a consistent differentiation scheme for determining intrinsic constituents of the tissue. The proposed technique has the benefit of being fully automatic, which could have significant advantages for automated histopathology and increasing the speed of intraoperative decisions. Validation of the technique is carried out with both phantom data and tissue samples of the human pancreas.
多维荧光成像是一种强大的分子成像方式，正在成为生物组织研究中的一种重要工具。由于与该技术相关的多光谱数据量很大，通常很难找到最佳参数组合以最大化不同组织类型之间的对比度。本文提出了一种基于时间分辨荧光成像来表征组织成分的新框架，无需对衰减进行显式建模。通过基于流形嵌入的软聚类来表征成分，以降低数据集的维度，并获得用于确定组织固有成分的一致区分方案。所提出的技术具有完全自动化的优点，这对于自动化组织病理学和提高术中决策速度可能具有显著优势。该技术通过模拟数据和人类胰腺组织样本进行了验证。
