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A CT-derived intravascular ultrasound simulation framework for deep learning-based image segmentation
Daniek van Aarle, Floor Fasen, Harold Schmeitz, Frits de Bruijn, Marc van Sambeek, Hans-Martin Schwab, Richard Lopata
Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00
Daniek van Aarle (Eindhoven University of Technology)
Floor Fasen (Eindhoven University of Technology)
Harold Schmeitz (Philips Research)
Frits de Bruijn (Philips Research)
Marc van Sambeek (Catharina Hospital Eindhoven; Eindhoven University of Technology)
Hans-Martin Schwab (Eindhoven University of Technology)
Richard Lopata (Eindhoven University of Technology)
Abstract:
Deep learning-based algorithms can extract patient-specific information on abdominal aortic aneurysms (AAAs) to improve current decision-making for clinical intervention. In this research we present a framework for the simulation of realistic two-dimensional intravascular ultrasound (IVUS) data based on Computed Tomography Angiography scans of the AAA. A segmentation network was trained on this synthetic dataset to prove the feasibility of deep learning-based image analysis on IVUS data.