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.

翻译：近年来，基于伪健康重建的无监督异常检测方法日益受到关注。该方法的最大优势在于无需繁琐的像素级数据标注，且可推广至包括罕见病在内的各类异常检测。通过仅使用健康受试者图像训练深度生成模型，模型能够学习重建伪健康图像，随后将此重建图像与原始输入进行对比以检测和定位异常。此类方法的评估通常依赖于测试数据中存在的真实病灶掩膜，但在某些应用场景中，该金标准可能缺失。为此，我们提出了一种基于真实异常图像模拟的评估流程，用于验证无金标准时伪健康重建方法的有效性。该方法能够系统性地测试生成模型在不同类型异常上的表现，并通过同一受试者的正常/异常图像对量化其性能。该框架可作为一种自动化初步验证手段，在需要临床专家介入的进阶验证之前，先评估生成模型重建伪健康图像的能力。我们以卷积变分自编码器在三维脑部FDG PET图像重建中的应用为例，旨在尽早检测痴呆症（如阿尔茨海默病）特异性神经退行性标志物。