Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets

Marta Gherardini, Evangelos B. Mazomenos, Arianna Menciassi, Danail Stoyanov

Abstract: Highlights•Fully-automated, real-time catheter and guidewire segmentation in fluoroscopy using CNNs.•Two-stage training strategy based on transfer learning technique, using synthetic images with predefined labelled segmentation.•Methods to reduce the need of manual pixel-level labelling to facilitate the development of CNN models for semantic segmentation, especially in the medical field.•Lightweight CNN model with a decreased number of network parameters which results in more efficient training and faster run times (84% reduction in testing time compared to the state-of-the-art).