Journal
FRONTIERS IN NEUROSCIENCE
Volume 15, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2021.793377
Keywords
corneal ulcer; GAN; slit-lamp image; semi-supervision; deep learning
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Funding
- National Key R&D Program of China [2018YFA0701700]
- National Nature Science Foundation of China [U20A20170, 61622114]
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In this paper, a novel semi-supervised multi-scale self-transformer generative adversarial network (Semi-MsST-GAN) is proposed for corneal ulcer segmentation in fluorescein staining of slit-lamp images. By introducing a multi-scale self-transformer network and a semi-supervised approach, the performance of corneal ulcer segmentation is improved.
Corneal ulcer is a common leading cause of corneal blindness. It is difficult to accurately segment corneal ulcers due to the following problems: large differences in the pathological shapes between point-flaky and flaky corneal ulcers, blurred boundary, noise interference, and the lack of sufficient slit-lamp images with ground truth. To address these problems, in this paper, we proposed a novel semi-supervised multi-scale self-transformer generative adversarial network (Semi-MsST-GAN) that can leverage unlabeled images to improve the performance of corneal ulcer segmentation in fluorescein staining of slit-lamp images. Firstly, to improve the performance of segmenting the corneal ulcer regions with complex pathological features, we proposed a novel multi-scale self-transformer network (MsSTNet) as the MsST-GAN generator, which can guide the model to aggregate the low-level weak semantic features with the high-level strong semantic information and adaptively learn the spatial correlation in feature maps. Then, to further improve the segmentation performance by leveraging unlabeled data, the semi-supervised approach based on the proposed MsST-GAN was explored to solve the problem of the lack of slit-lamp images with corresponding ground truth. The proposed Semi-MsST-GAN was comprehensively evaluated on the public SUSTech-SYSU dataset, which contains 354 labeled and 358 unlabeled fluorescein staining slit-lamp images. The results showed that, compared with other state-of-the-art methods, our proposed method achieves better performance with comparable efficiency.
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