Automatic prostate segmentation in MR images is essential in many clinical applications. Generative adversarial networks(GAN) have recently gained interests due to their promising ability in generating images which are difficult to distinguish from real images. In this paper, we propose an automatic and efficient algorithm base on GAN to segment the prostate contour and make the prostate segmentation shape more realistic. Our restult shows that the mean segmentation accuracy in test dataset is 90.3%±5.5. It indicates that the proposed strategy is feasible for segmentation of prostate MR images.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords