![]() Multimodal optical microscopy encompasses many imaging modalities often combined into an integrated system capable of simultaneously acquiring data from multiple contrast sources that are inherently co-registered both spatially and temporally. However, a quantitative understanding of the relationship carried between various modalities remains to be understood generally or utilized to better understand the subtle properties of biological specimens. In many cases, imaging modalities in a multimodal system are considered to provide complementary information, allowing multiple properties of the sample to be probed simultaneously. While multimodal optical microscopy continues to grow in popularity for characterizing complex biological systems and organisms, interpretation of the generated dense high-dimensional data can be difficult and highly subjective. In biological imaging, structural, functional, and molecular information regarding the specimen of interest can be obtained in a wide variety of settings including study of in vitro cell cultures ( 1), ex vivo tissue samples ( 2, 3), or even in vivo (intravital) imaging ( 4- 6). Multimodal optical microscopy is a collective set of imaging techniques well suited for high resolution, depth resolved, deep tissue imaging. Keywords: Optical imaging multimodal imaging image processing optical microscopy These methods are sensitive to the identification of diagnostic, cellular-level features important in a variety of clinical settings. ![]() 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.Ĭonclusions: 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. 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. ![]() 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. 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. However, the analysis of the dense, high-dimensional datasets acquired by these instruments remains mostly qualitative and restricted to analysis of each modality individually. Policy of Dealing with Allegations of Research Misconductīackground: 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.Policy of Screening for Plagiarism Process.
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