Fig. 2: Dataset-specific tasks drive reliable resolution enhancement with the TA-GAN approach.
a, Two TA-GAN models designed for the synaptic protein dataset are trained using one of two auxiliary tasks: the segmentation of the protein clusters (shown) or the localization of the weighted centroids (Supplementary Fig. 6). b, Comparison between the different approaches for the characterization of synaptic cluster morphological features. Shown is the cumulative distribution of the cluster area for PSD95 (see Supplementary Fig. 7 for other features). Statistical analysis: two-sided two-sample Kolmogorov–Smirnov test for the null hypothesis that the continuous distribution underlying the results for each baseline is the same as the one underlying the STED results (***P < 0.001, not significant (NS) P > 0.05). c, Representative crop chosen from one of the nine test images for the generation of synthetic two-colour images of PSD95 and bassoon using the non-task-assisted baseline (pix2pix), the TA-GANSyn with the localization task and the TA-GANSyn with the segmentation task. Insets: localization and segmentation annotations used to train the two TA-GANSyn models. Scale bars, 1 μm. Each crop is normalized to the 98th percentile of its pixel values for better visualization of dim clusters. d, The TA-GANSA models designed for the S. aureus dataset are trained using a segmentation task with annotations requiring only the LR bright-field image or annotations requiring the HR SIM image. e, Confusion matrices for the classification of dividing and non-dividing cells on the test set of the S. aureus dataset (n = 410 cells in five images). The TA-GANSA trained with HR annotations achieves better performance in generating the boundaries between dividing bacterial cell, a morphological feature visible only with SIM microscopy, compared with pix2pix and the TA-GANSA trained with LR annotations. f, Representative crop chosen from one of the five test images of the S. aureus dataset generated with pix2pix and the TA-GANSA trained with LR and HR annotations. Insets: LR and HR annotations used to train the two TA-GANSA models. Scale bars, 1 μm.
