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Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework.

Saurabh Jain1, Annemie Ribbens1, Diana M Sima2

  • 1icometrix Leuven, Belgium.

Frontiers in Neuroscience
|January 10, 2017
PubMed
Summary

MSmetrix-long accurately segments white matter lesions in multiple sclerosis (MS) using a novel joint expectation-maximization framework. This automated method improves lesion volume measurement consistency and reduces manual segmentation time.

Keywords:
MRIMSmetrixexpectation-maximizationlongitudinal lesion segmentationmultiple sclerosis

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Area of Science:

  • Medical Imaging
  • Neurology
  • Computational Biology

Background:

  • Lesion volume is crucial for multiple sclerosis (MS) prognosis.
  • Manual lesion segmentation is time-consuming and prone to observer variability.

Purpose of the Study:

  • To present MSmetrix-long, a joint expectation-maximization (EM) framework for segmenting white matter (WM) lesions in MS across two time points.
  • To improve accuracy and reproducibility in lesion volume measurement.

Main Methods:

  • MSmetrix-long utilizes 3D T1-weighted and 3D FLAIR MR images.
  • It employs a three-step process: cross-sectional segmentation, difference image creation for lesion evolution modeling, and a joint EM framework.
  • The method integrates outputs from initial segmentation and lesion evolution for final segmentation.

Main Results:

  • On the first dataset, MSmetrix-long achieved a median Dice score of 0.63 and a Pearson correlation coefficient (PCC) of 0.96 compared to expert segmentations.
  • On the second dataset, the median absolute lesion volume difference was 0.11 ml, demonstrating high reproducibility.

Conclusions:

  • MSmetrix-long provides accurate and consistent segmentation of MS lesions.
  • The performance of MSmetrix-long favorably compares to existing longitudinal MS lesion segmentation algorithms.