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Scanner-specific optimisation of automated lesion segmentation in MS.

David R van Nederpelt1, Giuseppe Pontillo2, Mar Barrantes-Cepas3

  • 1MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.

Neuroimage. Clinical
|October 8, 2024
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Summary

Optimizing automatic lesion segmentation tools for specific MRI scanners significantly improves their accuracy and reliability in people with multiple sclerosis (pwMS). Scanner-specific adjustments are crucial for consistent lesion detection across different machines.

Keywords:
AccuracyLesion segmentationMultiple sclerosisReliability

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Multiple Sclerosis Research

Background:

  • Automatic lesion segmentation on MRI aids multiple sclerosis (MS) research and clinical practice.
  • Limited knowledge exists on the reliability of these tools across different MRI scanners.
  • This variability hinders widespread clinical implementation of automated MS lesion segmentation.

Purpose of the Study:

  • To evaluate the within-scanner repeatability and between-scanner reproducibility of lesion segmentation tools in people with MS (pwMS).
  • To assess the accuracy of these tools compared to manual segmentations, with and without scanner-specific optimization.
  • To investigate the impact of optimization strategies on lesion segmentation performance across three MRI scanners.

Main Methods:

  • 30 pwMS underwent MRI scans (3D-FLAIR, 3D T1-weighted) and rescans on three scanners (GE Discovery MR750, Siemens Sola, Siemens Vida).
  • Lesion segmentation was performed using the Lesion Segmentation Toolbox (LST) and nicMSlesions (nicMS) with default and optimized settings.
  • Accuracy was measured by Dice Similarity Coefficient (DSC), and reliability by intra-class correlation coefficients (ICC).

Main Results:

  • Scanner-specific optimization significantly improved DSC for LST and nicMS compared to default settings.
  • Within-scanner repeatability for volume and counts was excellent (ICC > 0.9).
  • Between-scanner reproducibility varied, with higher ICCs observed between Siemens scanners and when using nicMS with scanner-specific optimization.

Conclusions:

  • Scanner-specific optimization strategies effectively reduce inter-scanner variability in lesion segmentation for pwMS.
  • These optimized approaches enhance the reproducibility and accuracy of automatic lesion segmentation tools.
  • Optimization is essential for reliable clinical implementation of automated MS lesion segmentation across diverse MRI systems.