PET-3DFlow: A Normalizing Flow Based Method for 3D PET Anomaly Detection

Zhe Xiong, Qiaoqiao Ding, Yuzhong Zhao, Xiaoqun Zhang

Abstract: Anomaly detection of Positron Emission Tomography (PET) are important tasks for clinical diagnosis and treatments. For 3D PET images, it is arduous for the annotations of lesions and the number of positive samples are generally very limited. Although there have been many work on deep learning based anomaly detection for medical images, most of them cannot be directly applied on 3D PET data. In this paper, from a set of normal PET data, we propose a tractable and efficient 3D normalizing flow based model, namely PET-3DFlow for anomaly detection and provide lesion localization for the reference of clinicians. The loss of In PET-3DFlow consists of the log-likelihood originated from the normalizing flow model and the reconstruction error of the autoencoder learned from the normal images. Experimental evaluation on 3D PET data showed that the proposed model outperformed the other approaches, which validated the effectiveness of our proposed PET anomaly detection method.