Accuracy versus Perception: a Benchmark of Deep Learning Models for Single-Image Super-Resolution in Microscopy

Virtual super-resolution deep learning methods provide a powerful solution to overcome the physical and temporal constraints of microscopy imaging. Yet, assessing and choosing an ideal methodological strategy complicates their use in life sciences and creates a lack of trust in these methods. Here we propose an objective comparison of nine popular single-image super-resolution (SISR) models in a collection of publicly available microscopy datasets, including cell components like microtubules, endoplasmic reticulum, and actin, using confocal microscopy, SEM, SIM, SMLM and STED microscopy modalities for fixed and live-cell microscopy data. The proposed models will be assessed quantitatively with a collection of metrics in microscopy and computer vision, and qualitatively by experts in the field. The proposed models will be made accessible through open, user-friendly, containerised notebooks. This systematic assessment of SISR approaches will provide a more comprehensive understanding of these methods' performance and contribute to standardising SISR methods in microscopy.

doi.org

Filopodome proteomics identifies CCT8 as a MYO10 interactor critical for filopodia functions

Cancer cells utilize filopodia to explore, adhere to, and invade their surrounding microenvironment, yet the protein networks that organize these protrusions remain incompletely defined. To uncover th...

biorxiv.org