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. 2025 Jan;93(1):384-396.
doi: 10.1002/mrm.30283. Epub 2024 Sep 2.

Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity

Xin Wang  1 , Gador Canton  2 , Yin Guo  3 , Kaiyu Zhang  3 , Halit Akcicek  4 , Ebru Yaman Akcicek  4 , Thomas Hatsukami  5 , Jin Zhang  6 , Beibei Sun  6 , Huilin Zhao  6 , Yan Zhou  6 , Linda Shapiro  7 , Mahmud Mossa-Basha  2 , Chun Yuan  2   4 , Niranjan Balu  2
Affiliations

Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity

Xin Wang et al. Magn Reson Med. 2025 Jan.

Abstract

Purpose: To develop an automated deep learning model for MRI-based segmentation and detection of intracranial arterial calcification.

Methods: A novel deep learning model under the variational autoencoder framework was developed. A theoretically grounded dissimilarity loss was proposed to refine network features extracted from MRI and restrict their complexity, enabling the model to learn more generalizable MR features that enhance segmentation accuracy and robustness for detecting calcification on MRI.

Results: The proposed method was compared with nine baseline methods on a dataset of 113 subjects and showed superior performance (for segmentation, Dice similarity coefficient: 0.620, area under precision-recall curve [PR-AUC]: 0.660, 95% Hausdorff Distance: 0.848 mm, Average Symmetric Surface Distance: 0.692 mm; for slice-wise detection, F1 score: 0.823, recall: 0.764, precision: 0.892, PR-AUC: 0.853). For clinical needs, statistical tests confirmed agreement between the true calcification volumes and predicted values using the proposed approach. Various MR sequences, namely T1, time-of-flight, and SNAP, were assessed as inputs to the model, and SNAP provided unique and essential information pertaining to calcification structures.

Conclusion: The proposed deep learning model with a dissimilarity loss to reduce feature complexity effectively improves MRI-based identification of intracranial arterial calcification. It could help establish a more comprehensive and powerful pipeline for vascular image analysis on MRI.

Keywords: calcification segmentation; deep learning; information bottleneck; intracranial arteries; variational autoencoders.

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Conflict of interest statement

Conflict of interest

The authors declare no potential conflict of interests.

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