Quality assessment of cortical surfaces, which is a crucial step and prerequisite in surface-based large-scale neuroimaging studies, aims to identify the low-quality surfaces and exclude them in the subsequent analysis. Convolutional neural network-based methods have achieved great success in image quality assessment, but they are inherently inapplicable for objects presented in non-Euclidean spaces, such as the brain cortical surfaces. To this end, we propose a transformer-based network, which describes the local information based on feature correspondences among vertices, thus enabling itself to be applied directly onto a spherical manifold. Extensive experiments on 1,860 infant cortical surfaces validated its superior performance.
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