Linda Vorberg: A Detailed Analysis on The Usefulness of CT for DL-Based Stroke Lesion Segmentation
Linda Vorberg, PhD Candidate at Siemens Healthineers CT and FAU Erlangen‘s Pattern Recognition Lab, shared on LinkedIn:
”I’m excited to share that our latest research article ‘Comparing deep learning stroke segmentation in NCCT, CTA, and CTP: Accuracy, domain transfer, and temporal sampling effect’ has been published in the Journal of Medical Physics!
Our work provides a detailed analysis on the usefulness of different CT image types for DL-based stroke lesion segmentation showing that advanced multitimepoint imaging significantly improves accuracy.
The study further emphasizes the need for cross-site adaptation to ensure robust clinical applicability.
I would like to sincerely thank all my co-authors and collaborators for their invaluable contributions, and continuous support throughout this project.
Special thanks to my supervisor, Dr. Oliver Taubmann, for his guidance throughout this journey.”
Title: Comparing deep learning stroke segmentation in NCCT, CTA, and CTP: Accuracy, domain transfer, and temporal sampling effect
Authors: Linda Vorberg, Hendrik Ditt, Andreas Maier, Savvas Nicolaou, Nicolas Murray, Oliver Taubmann
Read the Full Article on Journal of Medical Physics
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