Wavefront reconstruction based on deep transfer learning for microscopy

Abstract

The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.

Wavefront sensing of interference fringe based on generative adversarial network

“…Several researchers obtained results from focused light spot arrays measured by Shack-Hartmann sensors. [8,9] In several studies, the distributions of point spread functions (PSFs) were used to predict the magnitude of aberrations, [10][11][12][13][14] and have been extensively applied. In a previous study, aberration measurements were involved in the investigation of interference fringes.…”

Optimization of Spot Efficiency of Double-Helix Point Spread Function and Its Application in Intracellular Imaging

“…However, it requires complex optical devices and algorithms to measure the wavefront, which induces a high cost and increases the complexity of the system, hindering broader use [18,19]. The aberration optimization algorithm based on deep learning [20] can solve this problem to a certain extent. However, training the neural network requires a large number of samples and data.…”

Modal shape reconstruction method for deflectometry based on deep learning

“…Swanson et al [22] describe a deep-learning-based approach to the problem of wavefront reconstruction and prediction. Jin et al [23] propose a weights-sharing two-stream network for the prediction of the Zernike coefficient. Gu et al [24] presents a network based on ResNet50+ for reconstructing the wavefront with high accuracy.…”