ArticleQuantitative imaging in medicine and surgery2017
A quantitative framework for the analysis of multimodal optical microscopy images.
Article in Quantitative imaging in medicine and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- High-speed label-free two-photon fluorescence microscopy of metabolic transients during neuronal activity.Applied physics letters · 2021Article
- Article
- Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy.Biomedical optics express · 2019Article
- High-speed imaging of transient metabolic dynamics using two-photon fluorescence lifetime imaging microscopy.Optica · 2018Article
- Rapid tissue histology using multichannel confocal fluorescence microscopy with focus tracking.Quantitative imaging in medicine and surgery · 2018Article
- Advances in super-resolution photoacoustic imaging.Quantitative imaging in medicine and surgery · 2018Article
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Authors and funding
8 authors.
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Abstract
backgroundMultimodal 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.
methodsUsing 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.
resultsApplications of these techniques to the automated classification of cell death modes as well as to the identification of tissue components in fixed
conclusionsQuantification 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.
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