Automated Multi-Organ Segmentation In Pet Images Using Cascaded Training Of A 3D U-Net And Convolutional Autoencoder

PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structures of a patient and especially to perform some computer-aided diagnostics methods, e.g. segmentation of structures of interest. As this transfer of information from CT/MRI to the PET domain is not always feasible, e.g. when the corresponding CT or MRI images are unavailable or corrupted by artifacts, we propose a novel approach to perform organ segmentation on the PET images directly. We utilize a CNN architecture based on a 3D U-Net combined with a convolutional autoencoder and train our model purely on PET images and corresponding ground truth masks. Our resulting Dice scores of 0.88, 0.82 an 0.59 for liver, spleen and spine, respectively, show that standalone PET organ segmentation is generally feasible.