3D Reconstruction of cellular images from microfabricated imagers using fully-adaptive deep neural networks.

ABSTRACT: Millimeter-scale multi-cellular level imagers enable various applications, ranging from intraoperative surgical navigation to implantable sensors. However, the tradeoffs for miniaturization compromise resolution, making extracting 3D cell locations challenging-critical for tumor margin assessment and therapy monitoring. This work presents three machine-learning-based modules that extract spatial information from single image acquisitions using custom-made millimeter-scale imagers. The neural networks were trained on synthetically-generated (using Perlin noise) cell images. The first network is a convolutional neural network estimating the depth of a single layer of cells, the second is a deblurring module correcting for the point spread function (PSF). The final module extracts spatial inf

SUBMITTER: Najafiaghdam H 

PROVIDER: S-EPMC9068918 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature