Controllable Cardiac Synthesis via Disentangled Anatomy Arithmetic

Spyridon Thermos*, Xiao Liu, Alison O'Neil, Sotirios A. Tsaftaris

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a "latent vector arithmetic" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (vector) representations, we propose a framework termed "disentangled anatomy arithmetic", in which a generative model learns to combine anatomical factors of different input images such that when they are re-entangled with the desired imaging modality (e.g. MRI), plausible new cardiac images are created with the target characteristics. To encourage a realistic combination of anatomy factors after the arithmetic step, we propose a localized noise injection network that precedes the generator. Our model is used to generate realistic images, pathology labels, and segmentation masks that are used to augment the existing datasets and subsequently improve post-hoc classification and segmentation tasks. Code is publicly available at
Original languageEnglish
Publication statusAccepted/In press - 29 Sept 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention: MICCAI 2021 - Virtual
Duration: 27 Sept 20211 Oct 2021


Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI
Internet address

Keywords / Materials (for Non-textual outputs)

  • Disentangled anatomy arithmetic
  • semantic image synthesis
  • cardiac data augmentation


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