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Frontiers in Neuroscience|March 16, 2019
A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular DiseaseMichelle Livne, Jana Rieger, Orhun Utku Aydin, et al.Stroke|August 27, 2015
Comparison of the 2 Most Popular Deconvolution Techniques for the Detection of Penumbral Flow in Acute StrokeOlivier Zaro-Weber, Michelle Livne, Steve Z Martin, et al.Frontiers in Neurology|November 7, 2022
Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular diseaseAdam Hilbert, Jana Rieger, Vince I Madai, et al.Medical Image Analysis|March 1, 2022
Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networksPooja Subramaniam, Tabea Kossen, Kerstin Ritter, et al.PLOS Digital Health|February 22, 2023
To explain or not to explain?-Artificial intelligence explainability in clinical decision support systemsJulia Amann, Dennis Vetter, Stig Nikolaj Blomberg, et al.Frontiers in Artificial Intelligence|March 18, 2021
BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular DiseaseAdam Hilbert, Vince I Madai, Ela M Akay, et al.Journal of Medical Internet Research|July 13, 2021
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation StudyStanislav Nikolov, Sam Blackwell, Alexei Zverovitch, et al.Frontiers in Oncology|April 24, 2023
Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planningJ John Lucido, Todd A DeWees, Todd R Leavitt, et al.Pageof 2