Abstract
Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the applied voltage and the resulting axial focus position is not unambiguous. Adaptive lenses suffer from hysteresis effects, and their behaviour depends on environmental conditions. This is especially a hurdle when complex adaptive lenses are used that offer additional functionalities and are controlled with more degrees of freedom. In such case, a common approach is to iterate the voltage and monitor the adaptive lens. Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. We use the experimental data of our custom confocal microscope for training and validation. This leads to fast scanning without photo bleaching of the sample and opens the door to automatized and aberration-free smart microscopy. Applications in different types of laser-scanning microscopes are possible. However, maybe the training procedure of the neural network must be adapted for some use cases.
Highlights
Confocal microscopy (CM) is frequently used in life sciences to investigate biological samples, such as tissues and cells, where high spatial resolution is necessary to obtain information about small structures of the sample
In the last epoch of training, our model estimates the focal length on the validation data as mean squared error (MSE) of 128 μm2 from the corresponding ground truth on the validation data
We presented a modified implementation of the AlexNet architecture for a neural network to estimate the position of the focal plane in an adaptive confocal microscope based on an image of the illumination beam
Summary
Confocal microscopy (CM) is frequently used in life sciences to investigate biological samples, such as tissues and cells, where high spatial resolution is necessary to obtain information about small structures of the sample. The sample is moved through the constant focus by a stage as introduced in [3], though motion artefacts in the scan may occur. Another commercially available possibility for axial scanning is a piezo-driven objective holder [4]. Deformable mirrors (DM) may be used for axial scanning [5] and aberration correction [6,7] In recent years, another general approach based on adaptive lenses (AL) was introduced [2,8,9,10]. The most popular concepts include tunable acoustic gradient (TAG) lenses [11], liquid crystal lenses [12], lenses actuated by electrowetting [13], and deformable lenses with piezo actuators [10,14,15]
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