  Accurate choroid segmentation in optical coherence tomography (OCT) image is
vital because the choroid thickness is a major quantitative biomarker of many
ocular diseases. Deep learning has shown its superiority in the segmentation of
the choroid region but subjects to the performance degeneration caused by the
domain discrepancies (e.g., noise level and distribution) among datasets
obtained from the OCT devices of different manufacturers. In this paper, we
present an unsupervised perceptual-assisted adversarial adaptation (PAAA)
framework for efficiently segmenting the choroid area by narrowing the domain
discrepancies between different domains. The adversarial adaptation module in
the proposed framework encourages the prediction structure information of the
target domain to be similar to that of the source domain. Besides, a perceptual
loss is employed for matching their shape information (the curvatures of
Bruch's membrane and choroid-sclera interface) which can result in a fine
boundary prediction. The results of quantitative experiments show that the
proposed PAAA segmentation framework outperforms other state-of-the-art
methods.
