Signed graph representation learning for functional-to-structural brain network mapping

Tang, Haoteng; Guo, Lei; Fu, Xiyao; Wang, Yalin; Mackin, Scott; Ajilore, Olusola; Leow, Alex D.; Thompson, Paul M.; Huang, Heng; Zhan, Liang

doi:10.1016/j.media.2022.102674

Cited by 12 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enhancement is indicative of the complementary information offered by each contrast, collectively enriching the feature representation and enhancing the segmentation accuracy. Generally, the utilization of multi-contrast imaging holds significant promise in neuro-imaging research and clinical applications [63][64][65]. By leveraging the diverse information encapsulated within different contrasts, we can gain a more comprehensive understanding of the underlying tissue microstructure and pathology [34].…”

Section: Comparisons: Single-contrast Vs Multi-contrastmentioning

confidence: 99%

Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI

Tang,

Dai,

Zou

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and guiding surgical interventions. While most hippocampus segmentation studies focus on using T1-weighted or T2-weighted MRI scans, we explore the use of diffusion-weighted MRI (dMRI), which offers unique insights into the microstructural properties of the hippocampus. Particularly, we utilize various anisotropy measures derived from diffusion MRI (dMRI), including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, for a multi-contrast deep learning approach to hippocampus segmentation. To exploit the unique benefits offered by various contrasts in dMRI images for accurate hippocampus segmentation, we introduce an innovative multimodal deep learning architecture integrating cross-attention mechanisms. Our proposed framework comprises a multi-head encoder designed to transform each contrast of dMRI images into distinct latent spaces, generating separate image feature maps. Subsequently, we employ a gated cross-attention unit following the encoder, which facilitates the creation of attention maps between every pair of image contrasts. These attention maps serve to enrich the feature maps, thereby enhancing their effectiveness for the segmentation task. In the final stage, a decoder is employed to produce segmentation predictions utilizing the attention-enhanced feature maps. The experimental outcomes demonstrate the efficacy of our framework in hippocampus segmentation and highlight the benefits of using multi-contrast images over single-contrast images in diffusion MRI image segmentation.

show abstract

Section: Comparisons: Single-contrast Vs Multi-contrastmentioning

confidence: 99%