Deep Segmentation and Registration in X-Ray Angiography Video

Abstract

In interventional radiology, short video sequences of vein structure in motion are captured in order to help medical personnel identify vascular issues or plan intervention. Semantic segmentation can greatly improve the usefulness of these videos by indicating exact position of vessels and instruments, thus reducing the ambiguity. We propose a real-time segmentation method for these tasks, based on U-Net network trained in a Siamese architecture from automatically generated annotations. We make use of noisy low level binary segmentation and optical flow to generate multi class annotations that are successively improved in a multistage segmentation approach. We significantly improve the performance of a state of the art U-Net at the processing speeds of 90fps.

arXiv commentTo appear in BMVC 2018

References in corpus (3)

• Image Segmentation in Video Sequences: A Probabilistic Approach
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

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• Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease
• Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
• A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
• CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks
• End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention