Frontiers in Computational Neuroscience (Oct 2024)

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation

  • Kauê T. N. Duarte,
  • Kauê T. N. Duarte,
  • Abhijot S. Sidhu,
  • Abhijot S. Sidhu,
  • Murilo C. Barros,
  • David G. Gobbi,
  • David G. Gobbi,
  • Cheryl R. McCreary,
  • Cheryl R. McCreary,
  • Feryal Saad,
  • Richard Camicioli,
  • Richard Camicioli,
  • Eric E. Smith,
  • Mariana P. Bento,
  • Richard Frayne,
  • Richard Frayne,
  • Richard Frayne,
  • Richard Frayne

DOI
https://doi.org/10.3389/fncom.2024.1487877
Journal volume & issue
Vol. 18

Abstract

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IntroductionWhite matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets. Un-annotated data are relatively available; however, the requirement of annotated data poses a challenge for developing supervised machine learning models.MethodsTo address this challenge, we implemented a multi-stage semi-supervised learning (M3SL) approach that first uses un-annotated data segmented by traditional processing methods (“bronze” and “silver” quality data) and then uses a smaller number of “gold”-standard annotations for model refinement. The M3SL approach enabled fine-tuning of the model weights with the gold-standard annotations. This approach was integrated into the training of a U-Net model for WMH segmentation. We used data from three scanner vendors (over more than five scanners) and from both cognitively normal (CN) adult and patients cohorts [with mild cognitive impairment and Alzheimer's disease (AD)].ResultsAn analysis of WMH segmentation performance across both scanner and clinical stage (CN, MCI, AD) factors was conducted. We compared our results to both conventional and transfer-learning deep learning methods and observed better generalization with M3SL across different datasets. We evaluated several metrics (F-measure, IoU, and Hausdorff distance) and found significant improvements with our method compared to conventional (p < 0.001) and transfer-learning (p < 0.001).DiscussionThese findings suggest that automated, non-machine learning, tools have a role in a multi-stage learning framework and can reduce the impact of limited annotated data and, thus, enhance model performance.

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