Keywords: Diagnosis/Prediction, Brain, Graph Neural Network
Motivation: Building on previous work, this study aims to improve glioblastoma segmentation by focusing on complex tumor sub-regions, particularly in low-contrast areas.
Goal(s): Our goal is to enhance segmentation precision across tumor regions by combining Graph Neural Networks (GNNs) with diffusion models for stable multimodal MRI performance.
Approach: GNNs capture global and local features, creating coarse segmentation masks that guide U-Net performance on smaller regions. These representations are reprojected into volumetric space, with boundary penalties to refine accuracy.
Results: Our approach improves segmentation in challenging sub-regions, balancing performance on smaller regions and the overall tumor, supporting more precise glioblastoma treatment planning.
Impact: This study builds on prior work by combining diffusion models with graph neural networks to enhance brain tumor segmentation. By utilizing focused graph representations, the proposed method improves precision particularly within the tumor core and smaller subregions.
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