Learn Single-horizon Disease Evolution for Predictive Generation of
  Post-therapeutic Neovascular Age-related Macular Degeneration
Yuhan Zhang Kun Huang Mingchao Li Songtao Yuan Qiang Chen
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.