Fig 1.
Training of discriminator network.
For real examples, we used the real images and their segmentation/annotation masks (Mi, ) as an input. The green and red colored annotations correspond to Ki67 positive and Ki67 negative nuclei, respectively. For fake examples, we applied a two-step procedure. In Step 1, we used generator (U-net) algorithm to create a synthetic image by using the segmentation/annotation. In Step 2, the output of the generator and initial segmentation (Mi,
) are used as an input for D.
Fig 2.
Used neural network framework for generator, G.
Fig 3.
Fully synthetic images (e-g). We created several toy data to generate synthetic images with different characteristics by using annotation based input (a) and segmentation based input (b and c).
Fig 4.
Example (a) real image, (b) segmentation result based on [5], (c) synthetic image used for evaluation of computerized quantitative method, (d) visual ImmunoRatio output for the real image, visual ImmunoRatio output for synthetic image.
Table 1.
Experts’ discrimination performance on synthetic/real images.
TP represents the number of correctly identified synthetic images and TN represents the number of correctly identified real images.
Fig 5.
Example images (a) original image used for annotation (b) a dot based annotation, (c) cGAN generated synthetic image from (b). (d) Original image used for segmentation (e) segmentation result using [23], (f) cGAN generated image from (e).
Fig 6.
Bland-Altman plot comparing ImmunoRatio values of real and artificial images.
Shaded region corresponds to the limits of agreement.
Fig 7.
An example case where the immunoRatio values are different in the real and synthetic images.
The upper left nucleus in the real image (a) was missed by the segmentation result (b) based on [5]. Therefore the synthetic image (c) was not including that nucleus.