# Deep Segmentation and Registration in X-Ray Angiography Video

arXiv: https://arxiv.org/abs/1805.06406 · HTML: https://mlsanity.com/paper/1805.06406/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 9 of 9)

- [CathAction: A Benchmark for Endovascular Intervention Understanding](https://mlsanity.com/paper/2408.13126/) (2024)
- [Robust Implementation of Foreground Extraction and Vessel Segmentation for X-ray Coronary Angiography Image Sequence](https://mlsanity.com/paper/2209.07237/) (2022)
- [Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease](https://mlsanity.com/paper/2101.12505/) (2021)
- [Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation](https://mlsanity.com/paper/2106.11447/) (2021)
- [YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation](https://mlsanity.com/paper/2310.15898/) (2023)
- [CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks](https://mlsanity.com/paper/2106.07708/) (2021)
- [Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy](https://mlsanity.com/paper/2605.14253/) (2026)
- [Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms](https://mlsanity.com/paper/2609.04590/) (2026)
- [Novel Methods for Catheter and Guidewire Segmentation in X-ray Fluoroscopy under a Federated Learning Setting](https://mlsanity.com/paper/2609.06876/) (2026)

## Abstract (arXiv)

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.