  We present a deep learning framework for wide-field, content-aware estimation
of absorption and scattering coefficients of tissues, called Generative
Adversarial Network Prediction of Optical Properties (GANPOP). Spatial
frequency domain imaging is used to obtain ground-truth optical properties from
in vivo human hands, freshly resected human esophagectomy samples and
homogeneous tissue phantoms. Images of objects with either flat-field or
structured illumination are paired with registered optical property maps and
are used to train conditional generative adversarial networks that estimate
optical properties from a single input image. We benchmark this approach by
comparing GANPOP to a single-snapshot optical property (SSOP) technique, using
a normalized mean absolute error (NMAE) metric. In human gastrointestinal
specimens, GANPOP estimates both reduced scattering and absorption coefficients
at 660 nm from a single 0.2/mm spatial frequency illumination image with 58%
higher accuracy than SSOP. When applied to both in vivo and ex vivo swine
tissues, a GANPOP model trained solely on human specimens and phantoms
estimates optical properties with approximately 43% improvement over SSOP,
indicating adaptability to sample variety. Moreover, we demonstrate that GANPOP
estimates optical properties from flat-field illumination images with similar
error to SSOP, which requires structured-illumination. Given a training set
that appropriately spans the target domain, GANPOP has the potential to enable
rapid and accurate wide-field measurements of optical properties, even from
conventional imaging systems with flat-field illumination.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning