Deep learning-based holographic reconstruction for color imaging of pathology slides

Tairan Liu; Zhensong Wei; Yair Rivenson; Kevin de Haan; Yibo Zhang; Yichen Wu; Aydogan Ozcan

doi:10.1117/12.2567515

20 August 2020 Deep learning-based holographic reconstruction for color imaging of pathology slides

Tairan Liu, Zhensong Wei, Yair Rivenson, Kevin de Haan, Yibo Zhang, Yichen Wu, Aydogan Ozcan

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690E (2020) https://doi.org/10.1117/12.2567515
Event: SPIE Nanoscience + Engineering, 2020, Online Only

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

Citation Download Citation

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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