Consistent Brain Ageing Synthesis

Alzheimers Disease Neuroimaging Initiative

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

Abstract / Description of output

Brain ageing is associated with morphological changes and cognitive degeneration, and can be affected by neurodegenerative dis- eases which can accelerate the ageing process. The ability to separate accelerated from healthy ageing is useful from a diagnostic perspective and towards developing subject-specific models of progression. In this paper we start with the ‘simpler’ problem of synthesising age-progressed 2D slices. We adopt adversarial training to learn the joint distribution of brain images and ages, and simulate aged images by a network condi- tioned on age (a continuous variable) encoded as an ordinal embedding vector. We introduce a loss to help preserve subject identity despite that we train with cross-sectional (unpaired) data. To evaluate the quality of aged images, a pre-trained age predictor is used to estimate an appar- ent age. We show qualitatively and quantitatively that our method can progressively synthesise realistic brain images of different target ages.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention -MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China 13-17 October 2019
Publication statusPublished - 13 Oct 2019
Event22nd International Conference on Medical Image Computing and Computer Assisted Intervention - InterContinental Shenzhen, Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2019
Internet address


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