Keywords: Diagnosis/Prediction, Epilepsy
Motivation: This study seeks to address the challenge of limited visibility of periventricular lesions in Tuberous Sclerosis Complex (TSC).
Goal(s): Develop FLAIR3, a deep neural network, for adaptive fusion of T2w and FLAIR images in TSC patients to improve lesion detection.
Approach: The study adopts a dual-stream U-Net network with a pre-fusion module and employs spatial and channel fusion weight for feature fusion. Gradient loss and segmentation annotations are utilized to generate fusion images with clear textures and improved contrast.
Results: The fused image, FLAIR3, demonstrates enhanced lesion contrast and outperforms T2w and FLAIR images in lesion segmentation.
Impact: The enhanced lesion visualization provided by FLAIR3 can aid doctors in accurately identifying and diagnosing cortical tubers, improving the overall epilepsy diagnosis and treatment in TSC patients. This work improves the accuracy of automatic tuber segmentation.
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