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

• URL: http://arxiv.org/abs/2012.14230v1
• Date: Mon, 28 Dec 2020 13:48:21 GMT
• Title: Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
• Authors: Bo Li, Wiro J. Niessen, Stefan Klein, Marius de Groot, M. Arfan Ikram, Meike W. Vernooij, Esther E. Bron
• Abstract summary: Segis-Net is a single-step deep-learning framework for longitudinal image analysis. We applied Segis-Net to the analysis of white matter tracts from N045 longitudinal brain datasets of 3249 elderly individuals.
• Score: 10.548643411475584
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 comparing 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.

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