Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration

This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and...

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in...

Altered tract-specific white matter microstructure is related to poorer cognitive performance. The Rotterdam Study

White matter microstructural integrity has been related to cognition. Yet, the potential role of specificwhite matter tracts on top of a global white matter effect remains unclear, especially when consideringspecific cognitive domains. Therefore, we determined the tract-specific effect of white matter micro-structure on global cognition and...