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Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
EditorsDinggang Shen, Tianming Liu, Terry M Peters, Lawrence H Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Number of pages9
ISBN (Electronic)978-3-030-32248-9
ISBN (Print)978-3-030-32247-2
Publication statusE-pub ahead of print - 10 Oct 2019
EventThe 22nd International Conference on Medical Image Computing and Computer Assisted Intervention - InterContinental Shenzhen, Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Electronic)0302-9743


ConferenceThe 22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2019
City Shenzhen
Internet address


We propose a Generative Adversarial Network (GAN) model named disease evolution predictor GAN (DEP-GAN) to predict the evolution (i.e., progression and regression) of white matter hyperintensities (WMH) in small vessel disease. In this study, the evolution of WMH is represented by the “disease evolution map” (DEM) produced by subtracting irregularity map (IM) images from two time points: baseline and follow up. DEP-GAN uses two discriminators (critics) to enforce anatomically realistic follow up image and DEM. To simulate the nondeterministic and unknown parameters involved in WMH evolution, we propose modulating an array of random noises to the DEP-GAN’s generator which forces the model to imitate a wider spectrum of alternatives in the results. Our study shows that the use of two critics and random noises modulation in the proposed DEP-GAN improves its performance predicting the evolution of WMH in small vessel disease. DEP-GAN is able to estimate WMH volume in the follow up year with mean (std) estimation error of -1.91 (12.12) ml and predict WMH evolution with mean rate of 72.01% accuracy (i.e., 88.69% and 23.92% better than Wasserstein GAN).

    Research areas

  • Evolution of WMH, DEP-GAN, disease progression

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