Abstract
We present a deep-learning approach for solving the problem of 2π 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.
Highlights
The presence of phase ambiguities and their unwrapping algorithms constitute a wide field of research in both optical and non-optical imaging and sensing applications [1]
In the field of image processing, deep convolutional neural networks have revolutionized problems ranging from basic classification [26] and segmentation [27] to complex inverse problems in imaging [28,29,30,31,32,33,34,35]
The trained PhUn-Net is provided as an Open Neural Network Exchange (ONNX) file, which can run on many platforms, and is attached as a data file with this paper (Dataset 1 [45])
Summary
The presence of phase ambiguities and their unwrapping algorithms constitute a wide field of research in both optical and non-optical imaging and sensing applications [1]. QPI yields inherently high contrast for isolated cells in vitro without the need for staining, along with valuable quantitative information regarding both the internal geometrical structure and the refractive index (RI) distribution of the sample [4]. These key advantages led to the thriving of QPI as a leading imaging modality for both biological and medical research in the past decade [5,6,7,8,9]. The state of the art algorithms are computationally heavy, not readily available in all software platforms, and often difficult to implement
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