Computational imaging and intelligent specificity (Anastasio)

SUMMARY
In this technology research and development (TRD) project, advanced computational and machine learning
methods will be developed that address a variety of needs related to image formation and image analysis in
high-resolution label-free optical microscopy. Computational methods are being rapidly deployed that are
changing the way that measurement data are acquired and improving the formation and analysis of microscopy
images. The potential impact of such methods on the field of label-free microscopy is very high and can optimally
leverage inherent endogenous contrast mechanisms in innovative and informative ways. The developed
methods will serve as enabling technologies for many projects in the proposed center. The research will be
informed by and jointly developed and evaluated with the TRD and driving biological projects. A general theme
of this work is the integration of imaging science, physics- and deep learning (DL)-based approaches to
circumvent the limitations of label-free imaging and the use of objective image quality measures to systematically
validate and refine the developed methods. Three broad classes of computational methods will be investigated
that will enable the (1) image-to-image mapping of label-free images to provide computational specificity,
improved semantic segmentation, and/or enhanced spatial resolution; (2) improved reconstruction of images for
3D cellular imaging; and (3) extraction of biologically relevant information from multi-modality label-free image
data. The Specific Aims of the project are: Aim 1: Image-to-image translation methods for providing specificity,
semantic segmentation, and/or enhanced spatial resolution; Aim 2: Diffraction tomography and inverse
scattering methods for 3D imaging; and Aim 3: Biomarker discovery and multi-modal DL methods.
This successful completion of this project will result in computational and DL methods that will advance a variety
of label-free imaging technologies. These methods will enable improved computational staining, enhance of
spatial resolution, semantic segmentation, 3D image formation, and analysis of multi-modality label-free image
data. They will be systematically validated for use in the biomedical applications that are within the purview of
the proposed P41 center. All source code, trained models and documentation will be made open-source and
shared online.

Grant Number: 5P41EB031772-04
NIH Institute/Center: NIH
Principal Investigator: Mark Anastasio