Manual annotation of gliomas in magnetic resonance (MR) images is a laborious task, and it is impossible to identify active tumor regions not enhanced in the conventionally acquired MR modalities. Recently, quantitative MRI (qMRI) has shown capability in capturing tumor-like values beyond the visible tumor structure. Aiming at addressing the challenges of manual annotation, qMRI data was used to train a 2D U-Net deep-learning model for brain tumor segmentation. Results on the available data show that a 7% higher Dice score is obtained when training the model on qMRI post-contrast images compared to when the conventional MR images are used.
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