# Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels

arXiv: https://arxiv.org/abs/1709.02741 · HTML: https://mlsanity.com/paper/1709.02741/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography](https://mlsanity.com/paper/2204.08466/) (2022)
- [Nuclei Segmentation in Histopathology Images using Deep Learning with Local and Global Views](https://mlsanity.com/paper/2112.03998/) (2021)
- [Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation](https://mlsanity.com/paper/2112.14320/) (2021)
- [Evaluating Stenosis Detection with Grounding DINO, YOLO, and DINO-DETR](https://mlsanity.com/paper/2503.01601/) (2025)
- [Enhancing the Detection of Coronary Artery Disease Using Machine Learning](https://mlsanity.com/paper/2603.06888/) (2026)
- [Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms](https://mlsanity.com/paper/2609.04590/) (2026)

## Abstract (arXiv)

Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed segmentation method is based on superpixels. In this method at first three different superpixel scales are exploited and a measure for vesselness probability of each superpixel is determined. A majority voting is used for obtaining an initial segmentation map from these three superpixel scales. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it. Also we use the image ridges for extracting the coronary arteries centerlines. We evaluated our method qualitatively and quantitatively on two different challenging datasets and compared it with one of the previous well-known coronary arteries segmentation methods. Our method could detect the catheter and reduced the false positive rate in addition to achieving better segmentation results. The evaluation results prove that our method performs better in a much shorter time.