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Hilbert A, Rieger J, Madai VI, Akay EM, Aydin OU, Behland J, Khalil AA, Galinovic I, Sobesky J, Fiebach J, Livne M, Frey D. Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease. Front Neurol 2022; 13:1000914. [PMID: 36341105 PMCID: PMC9634733 DOI: 10.3389/fneur.2022.1000914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/22/2022] [Indexed: 11/21/2022] Open
Abstract
Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting.
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Affiliation(s)
- Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Adam Hilbert
| | - Jana Rieger
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Quality | Ethics | Open Science | Translation Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | - Ela M. Akay
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Orhun U. Aydin
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jonas Behland
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ahmed A. Khalil
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany
- Biomedical Innovation Academy, Berlin Institute of Health, Berlin, Germany
| | - Ivana Galinovic
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Sobesky
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Johanna-Etienne-Hospital, Neuss, Germany
| | - Jochen Fiebach
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Michelle Livne
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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