Longitudinal diffusion MRI analysis using Segis-Net
A single-step deep-learning framework for simultaneous segmentation and registration
Bo Li (Erasmus MC)
WJ Niessen (TU Delft - ImPhys/Imaging Physics, TU Delft - ImPhys/Computational Imaging, TU Delft - ImPhys/Medical Imaging, Erasmus MC)
Stefan Klein (Erasmus MC)
Marius de Groot (Erasmus MC)
Mohammad Arfan Ikram (Erasmus MC)
M. W. Vernooij (Erasmus MC)
E. E. Bron (Erasmus MC)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.
help_outline