Generative Adversarial Networks Wanyue Li - PowerPoint PPT Presentation

Jun 04, 2023 •44 likes •144 views

Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks Wanyue Li (wanyueli93@126.com) University of Science and Technology of China (USTC)

Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using 迁移学习中的领域自适应方法 Generative Adversarial Networks Wanyue Li (wanyueli93@126.com) • University of Science and Technology of China (USTC) • Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences
1 Motivation 2 Datasets OUTLINE 3 Method 4 Results 5 Conclusion
Motivation  Data from WHO shows that more than 2.2 billion people have a vision impairment or blindness so far.  Fluorescein angiography (FA) can reflect the damaged state of the retinal barrier in vivo eyes, and is regarded as the “gold standard” of retinal diseases diagnosis.  FA imaging has some potential serious adverse effects and is contraindicated for severe hypertension, heart disease, and etc. A method that can generate the corresponding FA image from structure image is needed. World report on vision. World Health Organization 2019.10 . Fluorescein and icg angiograms: still a gold standard. 85, 2007.
Datasets Image Collection (from hospital) Data Selection (late angiography) Data processing Multi-modal Obtain aligned Registration image pair
Method 𝑴 = 𝑴 𝑯𝒎𝒑𝒄𝒃𝒎 + 𝑴 𝑴𝒑𝒅𝒃𝒎 = (𝑴 𝑯𝑩𝑶 +𝜷𝑴 𝒒𝒋𝒚𝒇𝒎 + 𝜸𝑴 𝒒𝒇𝒔𝒅𝒇𝒒𝒖𝒗𝒃𝒎 ) + 𝜹𝑴 𝒕𝒃𝒎 𝑿 𝒋,𝒌 𝑰 𝒋,𝒌 𝟑 𝟐 𝒕𝒃𝒎 𝑴 𝒕𝒃𝒎 = 𝑱 𝑮 𝒚,𝒛 − 𝑯 𝜾 𝑯 𝑱 𝑻 𝒕𝒃𝒎 𝒚,𝒛 * Calculating process 𝑿 𝒋,𝒌 𝑰 𝒋,𝒌 of FA saliency map 𝒚=𝟐 𝒛=𝟐
Results – HRA dataset
Results – HRA dataset
Results – Infahan MISP dataset Normal FFA Abnormal FFA Structure The proposed Real FFA image CycleGAN Pix2Pix Without Lsal Without PatchGAN fundus image method Table 2 Performance comparison with different methods tested on Infahan MISP dataset Metrics CycleGAN Pix2Pix Without Without The proposed Lsal PatchGAN method PSNR(dB) 19.65 23.43 24.99 23.74 25.16 SSIM 0.5799 0.7438 0.7668 0.7471 0.8268
Conclusion Spotlight:  The proposed local saliency loss can ensure the accurate generation of the pathological structures in the synthesis FA image.  The data used to train and validate the proposed model were all selected according to the characteristics of fundus angiography and clinical demands, which can better demonstrate the medical significance of the proposed method. Limitation:  The proposed method performs unsatisfied on the leakage details generation.  Lack of a suitable and reliable measurement method to evaluate the reliability and value of the proposed method for physicians. The proposed method has better performance in retinal vascular and fluorescein leakages generation, which has great potential significance for clinical diagnosis.
Thanks for the MIDL 2020 organization and the Reviewers ！

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