Original Article
A quantitative framework for the analysis of multimodal optical microscopy images
Abstract
Background: Multimodal optical microscopy, a set of imaging techniques based on unique, yet complementary contrast mechanisms and spatially and temporally co-registered data acquisition, has emerged as a powerful biomedical tool. However, the analysis of the dense, high-dimensional datasets acquired by these instruments remains mostly qualitative and restricted to analysis of each modality individually.
Methods: Using a custom-built multimodal nonlinear optical microscope, high dimensional datasets were acquired for automated classification of functional cell states as well as identification of histopathological features in tissues slices. Supervised classification of cell death modes was performed through support vector machines (SVM) and semi-supervised classification of tissue slices was performed through the use of the expectation maximization (EM) algorithm.
Results: Applications of these techniques to the automated classification of cell death modes as well as to the identification of tissue components in fixed ex vivo tissue slices are presented. The analysis techniques developed provide a direct link between multimodal image contrast and biological structure and function, resulting in highly accurate classification in both settings.
Conclusions: Quantification of multimodal optical microscopy images through statistical modeling of the high dimensional data acquired gives a strong correlation between biological structure and function and image contrast. These methods are sensitive to the identification of diagnostic, cellular-level features important in a variety of clinical settings.
Methods: Using a custom-built multimodal nonlinear optical microscope, high dimensional datasets were acquired for automated classification of functional cell states as well as identification of histopathological features in tissues slices. Supervised classification of cell death modes was performed through support vector machines (SVM) and semi-supervised classification of tissue slices was performed through the use of the expectation maximization (EM) algorithm.
Results: Applications of these techniques to the automated classification of cell death modes as well as to the identification of tissue components in fixed ex vivo tissue slices are presented. The analysis techniques developed provide a direct link between multimodal image contrast and biological structure and function, resulting in highly accurate classification in both settings.
Conclusions: Quantification of multimodal optical microscopy images through statistical modeling of the high dimensional data acquired gives a strong correlation between biological structure and function and image contrast. These methods are sensitive to the identification of diagnostic, cellular-level features important in a variety of clinical settings.
