Global and local anomaly detectors for tumor segmentation in dynamic pet acquisitions

In this paper we explore the application of anomaly detection techniques to tumor voxels segmentation. The developed algorithms work on 3-points dynamic FDG-PET acquisitions and leverage on the peculiar anaerobic metabolism that cancer cells experience over time. A few different global or local anomaly detectors are discussed, together with an investigation over two different algorithms aiming to estimate normal tissues' statistical distribution. Finally, all the proposed algorithms are tested on a dataset composed of 9 patients proving that anomaly detectors are able to outperform techniques in the state of the art.