  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
based on the quantitative and qualitative evaluations. Compared with other
generative methods, the generative SD-OCT images of SHENet have the highest
image quality. Besides, SHENet achieves the best structure protection and
content prediction. 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.
