Table 1. Accuracy of 3D prediction of F-actin from retardance stack using different neural networks.

Above table lists median values of the Pearson correlation (r) and structural similarity index (SSIM) between prediction and target F-actin volumes. We report accuracy metrics for Slice→Slice (2D) ,Stack→Slice (2.5D), and Stack→Stack (3D) models trained to predict F-actin from retardance using Mean Absolute Error (MAE or L1) loss. We segmented target images with a Rosin threshold to discard tiles that mostly contained background pixels. To dissect the differences in prediction accuracy along and perpendicular to the focal plane, we computed (Materials and methods) test metrics separately over XY slices (r_xy, SSIM_xy) and XZ slices (r_xy, SSIM_xz) of the test volumes, as well as over entire test volumes (r_xyz, SSIM_xyz). Best performing model according to each metric is displayed in bold.

Translation model	Input(s)	rxy	rxz	rxyz	SSIMxy	SSIMxz	SSIMxyz
Slice→Slice (2D)	ρ	0.82	0.79	0.83	0.78	0.71	0.78
Stack→Slice (2.5D, $z=3$)	ρ	0.85	0.83	0.86	0.80	0.75	0.81
Stack→Slice (2.5D, $z=5$)	ρ	0.86	0.84	0.87	0.81	0.76	0.82
Stack→Slice (2.5D, $z=7$)	ρ	0.87	0.85	0.87	0.82	0.77	0.83
Stack→Stack (3D, $z=96$)	ρ	0.86	0.84	0.86	0.82	0.76	0.85