Presentation
20 August 2020 Deep learning-based holographic reconstruction for color imaging of pathology slides
Author Affiliations +
Abstract
We present a deep learning-based, high-throughput, accurate colorization framework for holographic imaging systems. Using a conditional generative adversarial network (GAN), this method can be used to remove the missing-phase-related spatial artifacts using a single hologram. When compared to the absorbance spectrum estimation method, which is the current state-of-the art method used to perform color holographic reconstruction, this framework is able to achieve a similar performance while requiring 4-fold fewer input images and 8-fold less imaging and processing time. The presented method can effectively increase the throughput for color holographic microscopy, providing the possibility for histopathology in resource limited environment.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tairan Liu, Zhensong Wei, Yair Rivenson, Kevin de Haan, Yibo Zhang, Yichen Wu, and Aydogan Ozcan "Deep learning-based holographic reconstruction for color imaging of pathology slides", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690E (20 August 2020); https://doi.org/10.1117/12.2567515
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KEYWORDS
Holography

Pathology

Color imaging

Imaging systems

3D image reconstruction

Microscopy

Tissues

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