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SYSTEM AND METHOD FOR TRANSFORMING HOLOGRAPHIC MICROSCOPY IMAGES TO MICROSCOPY IMAGES OF VARIOUS MODALITIES
SYSTEM AND METHOD FOR TRANSFORMING HOLOGRAPHIC MICROSCOPY IMAGES TO MICROSCOPY IMAGES OF VARIOUS MODALITIES
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机译:将全息显微图像转换成各种模态显微图像的系统和方法
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摘要
A trained deep neural network transforms an image of a sample obtained with a holographic microscope to an image that substantially resembles a microscopy image obtained with a microscope having a different microscopy image modality. Examples of different imaging modalities include bright-field, fluorescence, and dark-field. For bright-field applications, deep learning brings bright-field microscopy contrast to holographic images of a sample, bridging the volumetric imaging capability of holography with the speckle-free and artifact-free image contrast of bright-field microscopy. Holographic microscopy images obtained with a holographic microscope are input into a trained deep neural network to perform cross-modality image transformation from a digitally back-propagated hologram corresponding to a particular depth within a sample volume into an image that substantially resembles a microscopy image of the sample obtained at the same particular depth with a microscope having the different microscopy image modality.
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