Figure 3. Accuracy of 3D prediction with 2D, 2.5D, and 3D U-Nets.

Orthogonal sections (XY - top, XZ - bottom, YZ - right) of a glomerulus and its surrounding tissue from the test set are shown depicting (A) retardance (input image), (B) experimental fluorescence of F-actin stain (target image), and (C) Predictions of F-actin (output images) using the retardance image as input with different U-Net architectures. (D) Violin plots of structral-similarty metric (SSIM) between images of predicted and target stain in XY and XZ planes. The horizontal dashed lines in the violin plots indicate 25th quartile, median, and 75th quartile of SSIM. The yellow triangle in C highlights a tubule structure, whose prediction can be seen to improve as the model has access to more information along Z. The same field of view is shown in Figure 3—video 1, Figure 3—video 2, and Figure 4—video 1.

Figure 3—figure supplement 1. Schematic illustrating U-Net architectures.

Schematic of 2D U-Net model used for translating slice→slice and 2.5D U-Net model used for translating stack→slice. The 3D U-Net model used for translating stack→stack is similar to the 2D U-Net, but uses 3D convolutions instead of 2D and is four layers deep instead of 5 layers deep.

Figure 3—video 1. Z-stacks of brightfield, phase, retardance, and orientation images of mouse kidney tissue.

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The same field of view is shown in Figure 3, and Figure 4.

Figure 3—video 2. Through focus series showing 3D F-actin distribution in the test field of view shown in Figure 3.

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We show F-actin distribution (labeled with phalloidin-AF568) acquired on a confocal microscope (target), as well as predicted from 2D, 2.5D, and 3D models trained to translate retardance distribution into fluorescence distributions.