<p>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 optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility compared with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time.</p>

doi.org/10.1016/j.neuroimage.2021.118004, hdl.handle.net/1765/136166
Erasmus MC: University Medical Center Rotterdam

B. (Bo) Li, W.J. (Wiro) Niessen, S. (Stefan) Klein, M. (Marius) de Groot, M.A. (Arfan) Ikram, M.W. (Meike) Vernooij, & E.E. (Esther) Bron. (2021). Longitudinal diffusion MRI analysis using Segis-Net. NeuroImage, 235. doi:10.1016/j.neuroimage.2021.118004