The source code is publicly available on GitHub.
Bouchard, C., Wiesner, T., Deschênes, A., Bilodeau, A., Lavoie-Cardinal, F. & Gagné, C. Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition. bioRxiv (2023).
The preprint is available here.
We provide the datasets used in the paper. Just click on an image to download the pre-processed datasets with train/valid/test splits.
If you use the provided dataset please cite the following papers: Neuronal activity remodels the F-actin based submembrane lattice in dendrites but not axons of hippocampal neurons for the Axonal Rings dataset and the Dendritic F-actin Rings and Fibers Dataset, Activity-Dependent Remodeling of Synaptic Protein Organization Revealed by High Throughput Analysis of STED Nanoscopy Images for the Synaptic Proteins Dataset, Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition for Live F-Actin and Translated F-Actin. These datasets are available for general use by academic or non-profit, or government-sponsored researchers. This license does not grant the right to use these datasets or any derivation of it for commercial activities.
All the trained models that were used for the results presented in the paper are available on the project's GitHub repository, except the weights for the segmentation model for live F-actin (U-Net_live) which are available to download here. These pretrained weights were used to train TA-GAN_live and generate all segmentation predictions for live-cell images (Figures 3d, 3f, 4b, 5a, supplementary figures 12, 13, 14, 15, 16, 17, 18, 19, 20). These weights were also used in the acquisition loop of the STED microscope for TA-GAN assistance. The weights file is not included in the project's GitHub repository simply because the file is too large (167.3 MB).
Francine Nault and Sarah Pensivy for neuronal cell culture. Gabriel Leclerc for the FIJI macro for segmentation. Annette Schwerdtfeger for proofreading the manuscript. Funding was provided by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) (F.L.C. and C.G.), the Canada First Research Excellence Fund (F.L.C. and C.G.), the Canadian Institute for Health Research (CIHR) (F.L.C.), and the Neuronex Initiative (National Science Foundation 2014862, Fond de recherche du Québec - Santé) (F.L.C.). C.G. is a CIFAR Canada AI.