Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular...

Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions.Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk.

We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD). In SHENet, a feature encoder converts the input SD-OCT images to deep features, then a graph evolution module predicts the process of disease evolution in high-dimensional latent space and outputs the predicted deep features, and lastly, feature decoder recovers the predicted deep features to SD-OCT images. We further propose an evolution reinforcement module to ensure the effectiveness of disease evolution learning and obtain realistic SD-OCT images by adversarial training.

SHENet is validated on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes (P-0, P-1 and P-M) based on the quantitative and qualitative evaluations. Three metrics (PSNR, SSIM, 1-LPIPS) are used here for quantitative evaluations. Compared with other generative methods, the generative SD-OCT images of SHENet have the highest image quality (P-0: 23.659, P-1: 23.875, P-M: 24.198) by PSNR. Besides, SHENet achieves the best structure protection (P-0: 0.326, P-1: 0.337, P-M: 0.349) by SSIM and content prediction (P-0: 0.609, P-1: 0.626, P-M: 0.642) by 1-LPIPS. Qualitative evaluations also demonstrate that SHENet has a better visual effect than other methods.

SHENet can generate post-therapeutic SD-OCT images with both high prediction performance and good image quality, which has great potential to help ophthalmologists forecast the therapeutic effect of nAMD.