Phase imaging with computational specificity for cell biology applications

Recent advancements in quantitative phase imaging (QPI) and deep learning have opened up an exciting frontier. It has been shown that deep learning methods, with their ability to extract intricate structures from massive raw datasets, can be applied to both interpreting QPI measurements of biological samples and enhancing the imaging capabilities of QPI systems. Phase imaging with computational specificity (PICS), a workflow that combines deep learning and QPI, has recently been developed to nondestructively measure biophysical parameters or markers from label-free samples directly. In this thesis, we present a new non-invasive, high-throughput method built upon the principle of PICS, to detect the cell cycle of live cell clusters. We demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as investigate cell biophysical parameter distribution across different stages of the cell cycle.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• He, Yuchen

Contributors dc:contributor

• Popescu, Gabriel

• quantitative phase imaging
• deep learning
• phase imaging with computational specificity

Statement dc:rights

• Copyright 2021 Yuchen He

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/113100

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/113100