Abstract
Previous studies have indicated that white matter hyperintensities (WMH) may evolve, i.e., shrink, grow, or stay stable, over a period of time. However, predicting the evolution of WMH is challenging because the rate and direction of WMH evolution varies greatly across studies. Evolution of WMH also has a non-deterministic nature because some clinical factors that possibly influence it are still not known. In this study, we attempt to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later) using deep learning. We name this proposed model “Disease Evolution Predictor” (DEP). The DEP model receives a baseline image as input and produces a map called “Disease Evolution Map” (DEM), which represents the evolution of WMH from baseline to follow-up. Two models of DEP are proposed, i.e., DEP-UResNet and DEP-GAN, which represent supervised and unsupervised deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we propose modulating a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads were also tested. Based on our experiments, the supervised DEP-UResNet regularly performed better than the DEP-GAN. However, DEP-GAN using probability map (PM) yielded similar performances to the DEP-UResNet and performed best in clinical analysis. Furthermore, ablation study showed that auxiliary input, especially the Gaussian noise, improved the performance of DEP models regardless the model’s architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms and dealing with the non-deterministic nature of WMH evolution.
Footnotes
Adding qualitative (visual) analysis to the manuscript.