Deep Learning Combination of FLAIR and T2W for Improved TSC Lesion Detection

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.