ArticleFrontiers in neurology2023
Machine learning segmentation of core and penumbra from acute stroke CT perfusion data.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Deep Learning-Based Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Imaging.Biomimetics (Basel, Switzerland) · 2026Article
- Comparing deep learning stroke segmentation in NCCT, CTA, and CTP: Accuracy, domain transfer, and temporal sampling effect.Medical physics · 2026Article
- Core-Penumbra Hyperacute Ischemic Stroke Dataset.Scientific data · 2025Article
- Deep Learning-Based Prediction of Final Infarct Core from CT Perfusion Data: A Comparison to the Clinical Standard.Stroke (Hoboken, N.J.) · 2024Article
- Analysis of the role of perfusion parameters in predicting rupture of brain arteriovenous malformations: a multi-center study.Scientific reports · 2024Article
- Enhancing Stroke Recognition: A Comparative Analysis of Balance and Eyes-Face, Arms, Speech, Time (BE-FAST) and Face, Arms, Speech, Time (FAST) in Identifying Posterior Circulation Strokes.Journal of clinical medicine · 2024Article
- Reducing False-Positives in CT Perfusion Infarct Core Segmentation Using Contralateral Local Normalization.AJNR. American journal of neuroradiology · 2024Article
- Segmenting Ischemic Penumbra and Infarct Core Simultaneously on Non-Contrast CT of Patients with Acute Ischemic Stroke Using Novel Convolutional Neural Network.Biomedicines · 2024Article
- Clinical performance review for 3-D Deep Learning segmentation of stroke infarct from diffusion-weighted images.Neuroimage. Reports · 2024Article
- Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.Frontiers in neuroinformatics · 2024Article
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study.Tomography (Ann Arbor, Mich.) · 2023Review
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Authors and funding
8 authors.
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Abstract
Introduction: Computed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done Methods: We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images. Results: XGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy. Discussion: Further development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.
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