Title

A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution

Abstract

Phase contrast microscopy is a widely-used non-invasive technique for monitoring live cells over time. High-throughput biological experiments expect a wide-view (i.e., a low microscope magnification) to monitor the entire cell population and a high magnification on individual cell's details, which is hard to achieve simultaneously. In this paper, we propose a cascaded refinement Generative Adversarial Network (GAN) for phase contrast microscopy image super-resolution. Our algorithm uses an optic-related data enhancement and super-resolves a phase contrast microscopy image in a coarse-to-fine fashion, with a new loss function consisting of a content loss and an adversarial loss. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for super-resolving phase contrast microscopy images. The proposed algorithm provides a computational solution on achieving a high magnification on individual cell's details and a wide-view on cell populations at the same time, which will benefit the microscopy community.

Meeting Name

21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 (2018: Sep. 16-20, Granada, Spain)

Department(s)

Computer Science

Comments

This project was supported by NSF CAREER award 1351049 and NSF EPSCoR grant IIA-1355406.

Keywords and Phrases

Cell culture; Cell proliferation; Cells; Medical computing; Medical imaging; Optical resolving power, Adversarial networks; Biological experiments; Computational solutions; High magnifications; Microscope magnification; Noninvasive technique; Phase-contrast microscopy; Phase-contrast microscopy images, Image enhancement

International Standard Book Number (ISBN)

978-303000933-5

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Springer Verlag, All rights reserved.

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