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

ABSTRACT: Background and objectivesAutomated segmentation and tracking of surgical instruments and catheters under X-ray fluoroscopy hold the potential for enhanced image guidance in catheter-based endovascular procedures. This article presents a novel method for real-time segmentation of catheters and guidewires in 2d X-ray images. We employ Convolutional Neural Networks (CNNs) and propose a transfer learning approach, using synthetic fluoroscopic images, to develop a lightweight version of the U-Net architecture. Our strategy, requiring a small amount of manually annotated data, streamlines the training process and results in a U-Net model, which achieves comparable performance to the state-of-the-art segmentation, with a decreased number of trainable parameters. MethodsThe proposed transfer learn

SUBMITTER: Gherardini M 

PROVIDER: S-EPMC7903142 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature