ZFIN Image: Lauenburg et al., 2023, Fig. 1.

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Fig. 1.

Overview of the task and methods. (a) We aim to segment 3D instances in a completely unlabeled target domain IY by leveraging the images IX and masks SX in the source domain (i.e., unsupervised domain adaptation). Instead of (b) conducting image translation (e.g., via CycleGAN [9]) and instance segmentation as two separate steps, we propose (c) Cyclic Segmentation GAN (CySGAN) to unify the two functionalities using weight sharing, which is optimized with both image translation as well as supervised and semi-supervised segmentation losses.

Acknowledgments

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