Dardikman-Yoffe Gili, Roitshtain Darina, Mirsky Simcha K, Turko Nir A, Habaza Mor, Shaked Natan T
Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
Biomed Opt Express. 2020 Jan 24;11(2):1107-1121. doi: 10.1364/BOE.379533. eCollection 2020 Feb 1.
We present a deep-learning approach for solving the problem of 2 phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder-decoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.
我们提出了一种深度学习方法，用于解决生物细胞二维定量相位图中的二相模糊问题，该方法使用多层编码器-解码器残差卷积神经网络。我们在通过各种干涉测量设置捕获的各种类型的生物细胞以及模拟体模上测试了经过训练的网络PhUn-Net。这些测试证明了该网络的稳健性和通用性，即使对于形态不同或照明条件与PhUn-Net训练时不同的细胞也是如此。在本文中，我们首次以通用格式公开提供经过训练的网络，以便可以轻松地在每个平台上部署，从而实现快速且稳健的相位展开，而无需先验知识或复杂的实现。通过这种方式，我们期望我们的相位展开方法能够广泛应用，取代传统且更耗时的相位展开算法。
