Collagen orientation is one of the major indicators of cancer tumor aggressiveness and can be evaluated via tumor-associated collagen signature-3 (TACS-3). Quantitative phase imaging (QPI) is a rising imaging field in which the map associated with the optical pathlength through different regions of biological specimens are determined. Here we present the application of phase imaging with computational specificity (PICS) to image unlabeled collagen. Thus, we combine spatial light interference microscopy (SLIM), a highly sensitive QPI method, with an image-to-image deep neural network to extract phase-resolved collagen features from the input SLIM captures of histology tissue samples.
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