Biology subjects
Publications and source records attributed to Lightley, J..
We present a robust, low-cost single-shot implementation of differential phase microscopy utilising a polarisation-sensitive camera to simultaneously acquire 4 images from which the phase gradients and quantitative phase image can be calculated. This polarisation-resolved differential phase contrast (pDPC) microscopy technique can be interleaved with single-shot imaging polarimetry.
We present a robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, e.g. due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilise a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/- 100 m. We characterise the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localisation microscopy.