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  BRAVE-NET: Fully automated arterial brain vessel segmentation in patients with cerebrovascular disease

Hilbert, A., Madai, V. I., Akay, E. M., Aydin, O. U., Behland, J., Sobesky, J., et al. (2020). BRAVE-NET: Fully automated arterial brain vessel segmentation in patients with cerebrovascular disease. Frontiers in Artificial Intelligence, 3: 552258. doi:10.3389/frai.2020.552258.

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Hilbert, Adam1, Author
Madai, Vince I.1, 2, Author
Akay, Ela M.1, Author
Aydin, Orhun U.1, Author
Behland, Jonas1, Author
Sobesky, Jan3, 4, Author
Galinovic, Ivana3, Author
Khalil, Ahmed4, 5, 6, 7, Author           
Taha, Abdel A.8, Author
Wuerfel, Jens9, Author
Dusek, Petr10, Author
Niendorf, Tjoralf11, Author
Fiebach, Jochen B.3, Author
Frey, Dietmar1, Author
Livne, Michelle1, Author
1Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité University Medicine Berlin, Germany, ou_persistent22              
2School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment,University of Birmingham, United Kingdom, ou_persistent22              
3Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              
4Johanna-Etienne-Hospital, Neuss, Germany, ou_persistent22              
5Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
6Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
7Biomedical Innovation Academy, Berlin Institute of Health (BIH), Germany, ou_persistent22              
8Research Studio Data Science, Salzburg, Austria, ou_persistent22              
9Department Biomedical Engineering, Medical Image Analysis Center, University of Basel, Switzerland, ou_persistent22              
10Department of Neurology, First Faculty of Medicine, Charles University, Prague, Czech Republic, ou_persistent22              
11Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Germany, ou_persistent22              


Free keywords: Artificial intelligence (AI); Cerebrovascular disease (CVD); Machine learning; Segmentation (image processing); UNET
 Abstract: Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high- and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases. © Copyright © 2020 Hilbert, Madai, Akay, Aydin, Behland, Sobesky, Galinovic, Khalil, Taha, Wuerfel, Dusek, Niendorf, Fiebach, Frey and Livne.


Language(s): eng - English
 Dates: 2020-04-152020-08-252020-09-25
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3389/frai.2020.552258
Other: eCollection 2020
PMID: 33733207
PMC: PMC7861225
 Degree: -



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Title: Frontiers in Artificial Intelligence
Source Genre: Journal
Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 3 Sequence Number: 552258 Start / End Page: - Identifier: ISSN: 2624-8212
CoNE: https://pure.mpg.de/cone/journals/resource/2624-8212