2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-BSIP.5
Paper Title DISENTANGLED REPRESENTATION LEARNING FOR DEEP MR TO CT SYNTHESIS USING UNPAIRED DATA
Authors Runze Wang, Guoyan Zheng, Shanghai Jiao Tong University, China
SessionMLR-APPL-BSIP: Machine learning for biomedical signal and image processing
LocationArea C
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for biomedical signal and image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Many different methods have been proposed for generation of synthetic CT (sCT) from MR images. Most of these methods depend on paired-wise aligned MR and CT training images of the same patient, which are difficult to obtain. In this paper, we propose a novel disentangled representation learning method for MR to CT synthesis using unpaired data. Specifically, we first embed images onto two spaces: a modality- invariant geometry space capturing the shared anatomical information across different imaging domains, and a modality- specific appearance space. From the embedding, a sCT image can be synthesized from a MR image by taking the encoded geometry features from the MR image and an appearance vector sampled from the appearance space of a CT image. To handle the challenging of distinguishing cortical bone from air in MR images, where both of them have low intensity values, we propose a novel Geometry Similarity Module (GSM) to take the context information into consideration. Experimental results demonstrated that our approach achieved better or equivalent results than the state-of-the-art.