Unsupervised Positron Emission Tomography Tumor Segmentation via GAN based Adversarial Auto-Encoder

Fluorodeoxyglucose Positron emission tomography (FDG PET) is the imaging modality of choice for the diagnosis of lung cancer. The automated segmentation of tumors in PET images is a fundamental requirement for image analysis in computer aided diagnosis systems. Current tumor segmentation in PET generally relies on local features to discriminate tumor from the background. These methods are limited due to poor resolution, and subtle inter-class differences when there is normal FDG uptake region (i.e., in the heart and mediastinum) in the same field of view. We propose a new image based discriminative method to separate tumor regions from normal regions. We introduce a convolutional adversarial auto-encoder to learn a latent space which models normal (disease-free) variations of PET images, and then to compute a residual map that identifies where the PET image differs from this manifold due to anomalies, i.e., tumors. Our method is tolerant to normal intra-class variations among the PET images but is discriminative of the tumors with high sensitivity. Our experiments with a clinical lung cancer dataset show that our method outperformed the state-of-the-art unsupervised segmentation methods. We also achieved higher dice score (62.0%) and sensitivity (77.9%) than the supervised U-Net method (59.5% and 59.7%).