Unsupervised tumor detection in Dynamic PET/CT imaging of the prostate

Abstract

Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached. This work aims to detect prostate cancer foci in Dynamic PET images using an unsupervised learning approach. The proposed method extracts three feature classes from 4D imaging data that include statistical, kinetic biological and deep features that are learned by a deep stacked convolutional autoencoder. Anomalies, which are classified as tumors, are detected in feature space using density estimation. The proposed algorithm generates promising results for sufficiently large cancer foci in real PET scans imaging where the foci is not viewed by the tomographic devices used for detection.

Keywords: Autoencoder; Density estimation; Kinetic modeling; PET; Prostate.