  Over the past years, pseudo-healthy reconstruction for unsupervised anomaly
detection has gained in popularity. This approach has the great advantage of
not requiring tedious pixel-wise data annotation and offers possibility to
generalize to any kind of anomalies, including that corresponding to rare
diseases. By training a deep generative model with only images from healthy
subjects, the model will learn to reconstruct pseudo-healthy images. This
pseudo-healthy reconstruction is then compared to the input to detect and
localize anomalies. The evaluation of such methods often relies on a ground
truth lesion mask that is available for test data, which may not exist
depending on the application.
  We propose an evaluation procedure based on the simulation of realistic
abnormal images to validate pseudo-healthy reconstruction methods when no
ground truth is available. This allows us to extensively test generative models
on different kinds of anomalies and measuring their performance using the pair
of normal and abnormal images corresponding to the same subject. It can be used
as a preliminary automatic step to validate the capacity of a generative model
to reconstruct pseudo-healthy images, before a more advanced validation step
that would require clinician's expertise. We apply this framework to the
reconstruction of 3D brain FDG PET using a convolutional variational
autoencoder with the aim to detect as early as possible the neurodegeneration
markers that are specific to dementia such as Alzheimer's disease.
