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CySGAN: Cyclic Segmentation Generative Adversarial Network

Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps.

Data and Resources

Cite this as

Leander Lauenburg, Zudi Lin, Ruihan Zhang, M´arcia dos Santos, Siyu Huang, Ignacio Arganda-Carreras, Edward S. Boyden, Hanspeter Pfister, Donglai Wei (2024). Dataset: CySGAN: Cyclic Segmentation Generative Adversarial Network. https://doi.org/10.57702/r21dylur

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Author Leander Lauenburg
More Authors
Zudi Lin
Ruihan Zhang
M´arcia dos Santos
Siyu Huang
Ignacio Arganda-Carreras
Edward S. Boyden
Hanspeter Pfister
Donglai Wei
Homepage https://connectomics-bazaar.github.io/proj/CySGAN/index.html