Automatic localization of needles in real-time images can facilitate MR-guided percutaneous interventions. It enables automatic slice repositioning and targeting support and, thus, allows for faster workflows. Systematic acquisition of training data for deep learning tasks in the context of interventional MRI can be difficult due to the fact, that treatment quality must not be impaired. Therefore, we investigated whether images of porcine animal experiments can be used to train deep learning algorithms for needle artifact segmentation in human datasets. Results show that transfer is feasible at 1.5T. Additional fine tuning using small amounts of human data further reduces the error.
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