2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDBIO-6.6
Paper Title AUTOMATED MULTI-ORGAN SEGMENTATION IN PET IMAGES USING CASCADED TRAINING OF A 3D U-NET AND CONVOLUTIONAL AUTOENCODER
Authors Annika Liebgott, Charlotte Lorenz, University of Stuttgart, Germany; Sergios Gatidis, Viet Chau Vu, Konstantin Nikolaou, University Hospital of Tuebingen, Germany; Bin Yang, University of Stuttgart, Germany
SessionBIO-6: Medical Image Segmentation
LocationGather.Town
Session Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Time:Wednesday, 09 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO-MIA] Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract 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.