A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution
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.
L. Han and Z. Yin, "A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11071 LNCS, pp. 347 - 355, Springer Verlag, Sep 2018.
The definitive version is available at https://doi.org/10.1007/978-3-030-00934-2_39
21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 (2018: Sep. 16-20, Granada, Spain)
Intelligent Systems Center
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
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Article - Conference proceedings
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01 Sep 2018