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Fig. 2.

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ZDB-FIG-230916-83

Publication

Lauenburg et al., 2023 - 3D Domain Adaptive Instance Segmentation Via Cyclic Segmentation GANs

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Fig. 2.

Architecture details of CySGAN. Given an image sampled from IY, the generator G predicts both the transferred image in IX and the BCD segmentation representations SY. Then the generator F takes only the translated image as input and predicts both the reconstructed image and segmentation representations. Specifically, BCD stands for “binary foreground mask, “contour map,” and “distance transform map.” We visualize the predicted BCD representations in the dashed yellow boxes. The two generators have exactly the same architecture, but the weights are not shared as they are optimized to translate images in different domains. Only the generator G is needed to segment IY images at inference time (the output channel for translation can also be removed).

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Acknowledgments

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