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Published on: June 9, 2018
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.
Abstract:
White matter hyperintensities (WMH) detected on FLAIR MRI sequences serve as important biomarkers for cerebrovascular pathology, correlating with increased risks of cognitive decline, stroke, and demyelination. Contemporary automated segmentation approaches face substantial challenges in distinguishing pathological lesions from normal age-related periventricular hyperintensities, resulting in elevated false-positive rates that limit clinical utility. This investigation examines whether incorporating normal WMH as an explicit class during deep learning model training enhances pathological lesion detection compared to conventional binary segmentation approaches. We evaluated four established architectures (U-Net, Attention U-Net, DeepLabV3Plus, and Trans-U-Net) across two training paradigms using 2,750 FLAIR images from 115 patients with neurodegenerative diseases, sourced from local and public datasets with expert radiological annotations. The first paradigm employed traditional binary classification (background versus pathological WMH), while the second utilized multiclass classification incorporating normal periventricular hyperintensities as a distinct category. Statistical evaluation included paired comparative analysis and effect size quantification using Cohen's d. The U-Net architecture demonstrated the most pronounced improvement with the multiclass approach, achieving 0.271 improvement in Dice coefficient (0.768 versus 0.497) and 1.9 improvement in Hausdorff distance (11.5 vs 13.4) (p < 0.0001, Cohen's d = 0.5643). All architectures demonstrated medium practical effects (d = 0.44-0.57) beyond statistical significance. In the present data regime, convolutional neural network-based architectures demonstrated more stable training dynamics and larger performance improvements compared to the transformer-based models, though all architectures showed statistically significant benefits from multiclass training. The multiclass training methodology substantially improves pathological WMH identification while preserving clinical practicality, offering a robust framework for enhancing automated neuroimaging diagnostic capabilities.Trial Registration Number Tabriz University of Medical Sciences Research Ethics Committee (IR.TBZMED.REC.1402.902).
Insights
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.
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.
