Siân Culley

This paper introduces TomoFLIM, a high-speed multiphoton FLIM technique that enables rapid, accurate imaging of dynamic biological processes with compression ratios over 90%.

This paper introduces SQUASSH, a convolutional neural network that fits structural models to fluorescence microscopy data to quantify variability across biological scales.

The paper shows that image quality metrics (IQMs) are unreliable for guiding fluorescence microscopy image processing as they do not correlate with downstream biological analysis performance.

NanoJ provides a high-performance open-source toolbox for super-resolution microscopy, offering tools for data alignment, image reconstruction, quality assessment, structural modeling, and sample control.

This paper demonstrates how deep learning-based image restoration extends fluorescence microscopy's capabilities by improving resolution, reducing photon exposure, and increasing frame rates.