Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge

BACKGROUND AND PURPOSE: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary t...

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Detalles Bibliográficos
Autores principales: Hakim, Arsany, Christensen, Søren, Winzeck, Stefan, Lansberg, Maarten G., Parsons, Mark W., Lucas, Christian, Robben, David, Wiest, Roland, Reyes, Mauricio, Zaharchuk, Greg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Original Contributions
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240494/
https://www.ncbi.nlm.nih.gov/pubmed/33957774
http://dx.doi.org/10.1161/STROKEAHA.120.030696

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240494/
https://www.ncbi.nlm.nih.gov/pubmed/33957774
http://dx.doi.org/10.1161/STROKEAHA.120.030696

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