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Related Experiment Video

Updated: Nov 10, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous

Bo Li1, Wiro J Niessen2, Stefan Klein1

  • 1Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.

Neuroimage
|April 1, 2021
PubMed
Summary
This summary is machine-generated.

Segis-Net, a novel deep learning framework, enhances longitudinal brain MRI analysis by simultaneously performing segmentation and registration. This approach improves accuracy and reduces required sample sizes for studying brain changes over time.

Keywords:
CNNDeep learningDiffusion MRILongitudinalRegistrationSegmentationWhite matter tract

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Longitudinal studies are crucial for understanding brain changes over time.
  • Current multi-stage pipelines for analyzing longitudinal neuroimaging data can be complex and less accurate.
  • Optimizing both segmentation and registration simultaneously is key to improving analysis.

Purpose of the Study:

  • To introduce Segis-Net, a single-step deep learning framework for longitudinal image analysis.
  • To concurrently learn multi-class segmentation and nonlinear registration for enhanced information exploitation.
  • To develop an objective function optimizing spatial correspondence across time-points.

Main Methods:

  • Developed Segis-Net, a convolutional neural network-based framework.
  • Simultaneously optimized segmentation and registration for mutual benefit.
  • Applied the framework to 8045 longitudinal brain MRI datasets from 3249 elderly individuals.

Main Results:

  • Segis-Net demonstrated significant improvements in registration accuracy.
  • Achieved enhanced spatio-temporal segmentation consistency and reproducibility compared to multi-stage pipelines.
  • Showed a significant reduction in the sample size needed for statistical power in tract-specific analyses.

Conclusions:

  • Segis-Net offers a reliable, single-step solution for longitudinal neuroimaging analysis.
  • The framework facilitates more efficient and accurate investigation of brain structure changes.
  • Segis-Net is a promising tool for future longitudinal brain imaging research.