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

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 netwo...

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Veröffentlicht in:	Magnetic resonance in medicine 2025-01, Vol.93 (1), p.384-396
Hauptverfasser:	Wang, Xin, Canton, Gador, Guo, Yin, Zhang, Kaiyu, Akcicek, Halit, Yaman Akcicek, Ebru, Hatsukami, Thomas, Zhang, Jin, Sun, Beibei, Zhao, Huilin, Zhou, Yan, Shapiro, Linda, Mossa‐Basha, Mahmud, Yuan, Chun, Balu, Niranjan
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Algorithms Arteriosclerosis Automation Brain - diagnostic imaging Calcification Calcification (ectopic) calcification segmentation Complexity Deep Learning Feature extraction Female Humans Image analysis Image Interpretation, Computer-Assisted - methods Image processing Image Processing, Computer-Assisted - methods Image segmentation information bottleneck intracranial arteries Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Metric space Middle Aged Recall Segmentation Statistical analysis Statistical tests variational autoencoders Vascular Calcification - diagnostic imaging
Online-Zugang:	Volltext
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