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No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network
- No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network
- Jaferzadeh, Keyvan; Hwang, Seung-Hyeon; Moon, Inkyu; Javidi, Bahram
- DGIST Authors
- Moon, Inkyu
- Issue Date
- Biomedical Optics Express, 10(8), 4276-4289
- Article Type
- PHASE-CONTRAST MICROSCOPY; 3-DIMENSIONAL IDENTIFICATION; REFRACTIVE-INDEX; LIVING CELLS; QUANTIFICATION; MORPHOMETRY
- Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
- The Optical Society
- Related Researcher
Intelligent Imaging and Vision Systems Laboratory
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- Department of Robotics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles
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