Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing deep learning-based phase retrieval methods have technical limitations in generalization performance and three-dimensional (3D) morphology reconstruction from a single-shot hologram of biological cells. In this study, we propose a novel deep learning model, named MorpHoloNet, for single-shot reconstruction of 3D morphology by integrating physics-driven and coordinate-based neural networks. By simulating the optical diffraction of coherent light through a 3D phase shift distribution, the proposed MorpHoloNet is optimized by minimizing the loss between the simulated and input holograms on the sensor plane. Compared to existing DIHM methods that face challenges with twin image and phase retrieval problems, MorpHoloNet enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram without requiring multiple phase-shifted holograms or angle scanning. The performance of the proposed MorpHoloNet is validated by reconstructing 3D morphologies and refractive index distributions from synthetic holograms of ellipsoids and experimental holograms of biological cells. The proposed deep learning model is utilized to reconstruct spatiotemporal variations in 3D translational and rotational behaviors and morphological deformations of biological cells from consecutive single-shot holograms captured using DIHM. MorpHoloNet would pave the way for advancing label-free, real-time 3D imaging and dynamic analysis of biological cells under various cellular microenvironments in biomedical and engineering fields.

本研究解决了现有深度学习相位恢复方法在生物细胞单次全息图三维形态重建中的泛化性能和技术限制问题。提出的MorpHoloNet模型通过结合物理驱动与基于坐标的神经网络，实现了从单次全息图直接重建三维复杂光场与形态的创新方法。该模型经过验证，能够从合成与实验全息图中有效重建生物细胞的三维形态，为生物医学和工程领域的无标记实时三维成像和动态分析开辟了新路径。

Single-shot reconstruction of three-dimensional morphology of biological
  cells in digital holographic microscopy using a physics-driven neural network

采用物理驱动神经网络在数字全息显微镜中单次重建生物细胞三维形态