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.

基于模拟现实异常图像的伪健康重建方法的评估程序，旨在提前检测特定于痴呆症如阿尔茨海默病的神经退行性标志物。

基于深度生成模型的假健康图像重建用于异常检测的评估：以脑部 FDG PET 为例

Evaluation of pseudo-healthy image reconstruction for anomaly detection  with deep generative models: Application to brain FDG PET

Unsupervised anomaly detection is a popular approach for the analysis of
neuroimaging data as it allows to identify a wide variety of anomalies from
unlabelled data. It relies on building a subject-specific model of healthy
appearance to which a subject's image can be compared to detect anomalies. In
the literature, it is common for anomaly detection to rely on analysing the
residual image between the subject's image and its pseudo-healthy
reconstruction. This approach however has limitations partly due to the
pseudo-healthy reconstructions being imperfect and to the lack of natural
thresholding mechanism. Our proposed method, inspired by Z-scores, leverages
the healthy population variability to overcome these limitations. Our
experiments conducted on FDG PET scans from the ADNI database demonstrate the
effectiveness of our approach in accurately identifying Alzheimer's disease
related anomalies.

我们提出了一种受 Z 分数启发的方法，利用健康人群的变异性来识别阿尔茨海默病相关异常，实验证明了我们方法的有效性。