CM2Net: a deep learning  framework for computational miniature mesoscope (Conference Presentation)

Qianwan Yang; Yujia Xue; Guorong Hu; Kehan Guo; Lei Tian

doi:10.1117/12.2632322

4 October 2022 CM2Net: a deep learning framework for computational miniature mesoscope (Conference Presentation)

Qianwan Yang, Yujia Xue, Guorong Hu, Kehan Guo, Lei Tian

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040D (2022) https://doi.org/10.1117/12.2632322
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Computational Miniature Mesoscope (CM2) is a novel fluorescence imaging device that achieves single-shot 3D imaging on a compact platform by jointly designing the optics and algorithm. However, the low axial resolution and heavy computational cost hinder its biomedical applications. Here, we demonstrate a deep learning framework, termed CM2Net, to perform fast and reliable 3D reconstruction. Specifically, the multi-stage CM2Net is trained on synthetic data with realistic field varying aberrations based on a 3D linear shift variant model. We experimentally demonstrate that the CM2Net can provide 10x improvement in axial resolution and 1400x faster reconstruction speed.

Conference Presentation

Citation Download Citation

Qianwan Yang, Yujia Xue, Guorong Hu, Kehan Guo, and Lei Tian "CM2Net: a deep learning framework for computational miniature mesoscope (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040D (4 October 2022); https://doi.org/10.1117/12.2632322

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