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Incorporating normal periventricular changes for enhanced pathological white matter hyperintensity segmentation: on

Mahdi Bashiri Bawil1, Mousa Shamsi2, Ali Fahmi Jafargholkhanloo3

  • 1Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran. mehdi.bashiri.bawil@gmail.com.

Biomedical Engineering Online
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

Multiclass deep learning improves detection of pathological white matter hyperintensities (WMH) by distinguishing them from normal WMH. This enhances diagnostic accuracy for cerebrovascular pathology in neuroimaging.

Keywords:
Deep learningFLAIR MRIMedical image segmentationNeuroimagingPathological segmentationU-NetWhite matter hyperintensities (WMH)

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Cerebrovascular Diseases

Background:

  • White matter hyperintensities (WMH) on FLAIR MRI are key biomarkers for cerebrovascular pathology, linked to cognitive decline and stroke.
  • Current automated segmentation methods struggle to differentiate pathological WMH from normal age-related changes, leading to high false-positive rates and limited clinical use.

Purpose of the Study:

  • To investigate if incorporating normal WMH as a distinct class in deep learning training improves pathological WMH detection compared to binary segmentation.
  • To evaluate the performance of different deep learning architectures under multiclass versus binary segmentation paradigms.

Main Methods:

  • Four deep learning architectures (U-Net, Attention U-Net, DeepLabV3Plus, Trans-U-Net) were trained using two paradigms: binary (background vs. pathological WMH) and multiclass (background, normal WMH, pathological WMH).
  • The study utilized 2,750 FLAIR MRI images from 115 patients with neurodegenerative diseases, with expert radiological annotations.
  • Performance was statistically evaluated using paired comparative analysis and Cohen's d for effect size.

Main Results:

  • The U-Net architecture showed the most significant improvement with multiclass training, enhancing the Dice coefficient by 0.271 (0.768 vs. 0.497) and improving Hausdorff distance (11.5 vs. 13.4, p < 0.0001).
  • All evaluated architectures demonstrated statistically significant benefits and medium practical effects (Cohen's d = 0.44-0.57) from multiclass training.
  • Convolutional neural network-based architectures exhibited more stable training and greater performance gains than transformer-based models in this dataset.

Conclusions:

  • Multiclass training, explicitly including normal WMH, substantially enhances the identification of pathological WMH.
  • This approach offers a robust framework for improving automated neuroimaging diagnostics, maintaining clinical practicality.
  • The findings support the clinical utility of advanced deep learning techniques for accurate WMH segmentation in neurodegenerative disease assessment.