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However, OCT images are often acquired by different devices or under different imaging protocols, which leads to serious domain shift problem. This in turn results in performance degradation of segmentation models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Aiming at the domain shift problem, we propose a two\u2010stage adversarial learning based network (TSANet) that accomplishes unsupervised cross\u2010domain OCT segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In the first stage, a Fourier transform based approach is adopted to reduce image style differences from the image level. Then, adversarial learning networks, including a segmenter and a discriminator, are designed to achieve inter\u2010domain consistency in the segmentation output. In the second stage, pseudo labels of selected unlabeled target domain training data are used to fine\u2010tune the segmenter, which further improves its generalization capability. The proposed method was tested on cross\u2010domain datasets for choroid or retinoschisis segmentation tasks. For choroid segmentation, the model was trained on 400 images and validated on 100 images from the source domain, and then trained on 1320 unlabeled images and tested on 330 images from target domain I, and also trained on 400 unlabeled images and tested on 200 images from target domain II. For retinoschisis segmentation, the model was trained on 1284 images and validated on 312 images from the source domain, and then trained on 1024 unlabeled images and tested on 200 images from the target domain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method achieved significantly improved results over that without domain adaptation, with improvement of 8.34%, 55.82% and 3.53% in intersection over union (IoU) respectively for the three test sets. The performance is better than some state\u2010of\u2010the\u2010art domain adaptation methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed TSANet, with image level adaptation, feature level adaptation and pseudo\u2010label based fine\u2010tuning, achieved excellent cross\u2010domain generalization. This alleviates the burden of obtaining additional manual labels when adapting the deep learning model to new OCT data.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17012","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T12:20:31Z","timestamp":1709295631000},"page":"5374-5385","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Two\u2010stage adversarial learning based unsupervised domain adaptation for retinal OCT segmentation"],"prefix":"10.1002","volume":"51","author":[{"given":"Shengyong","family":"Diao","sequence":"first","affiliation":[{"name":"MIPAV Lab, the School of Electronics and Information Engineering Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziting","family":"Yin","sequence":"additional","affiliation":[{"name":"MIPAV Lab, the School of Electronics and Information Engineering Soochow University  Suzhou 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