AUTHOR=Chen Ting , You Wei , Zhang Liyuan , Ye Wanxing , Feng Junqiang , Lu Jing , Lv Jian , Tang Yudi , Wei Dachao , Gui Siming , Jiang Jia , Wang Ziyao , Wang Yanwen , Zhao Qi , Zhang Yifan , Qu Junda , Li Chunlin , Jiang Yuhua , Zhang Xu , Li Youxiang , Guan Sheng
TITLE=Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography
JOURNAL=Frontiers in Physiology
VOLUME=14
YEAR=2024
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1310357
DOI=10.3389/fphys.2023.1310357
ISSN=1664-042X
ABSTRACT=Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs).
Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model’s potential in enhancing clinicians’ precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss’ Kappa) were calculated to assess the differences in clinical performance of clinicians.
Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians’ mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss’ Kappa) increased by 0.07 (p = 0.029).
Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.