2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-1.1
Paper Title AN ADVERSARIAL COLLABORATIVE-LEARNING APPROACH FOR CORNEAL SCAR SEGMENTATION WITH OCULAR ANTERIOR SEGMENT PHOTOGRAPHY
Authors Ke Wang, Tsinghua University, China; Guangyu Wang, Beijing University of Posts and Telecommunications, Macao SAR of China; Kang Zhang, Macau University of Science and Technology, Macao SAR of China; Chen Ting, Tsinghua University, China
SessionBIO-1: Biomedical Signal Processing 1
LocationArea C
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
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Abstract Corneal scarring is a common eye disease that leads to reduced vision. An accurate diagnosis and segmentation of corneal scar is a critical in ensuring proper treatment. Deep neural networks have made great progress in medical image segmentation, but the training requires large amount of annotated data. Pixel-level corneal scar can only be annotated by experienced ophthalmologists, but eye structure annotation can be done easily by people with minimal medical knowledge. In this paper, we propose Dual-Eye-GAN Net (DEG-Net), an end-to-end adversarial collaborative-learning corneal scar segmentation model. DEG-Net can improve segmentation quality with additional data that only has eye structure annotation. We collect the first corneal scar segmentation dataset in the form of anterior ocular photography. Experimental results demonstrate superiority to both supervised and semi-supervised approaches. This is the first empirical study on corneal scar segmentation with anterior ocular photography. The code and dataset can be found in \url{https://github.com/kaisadadi/Dual-GAN-Net}.