# Learning-Based Algorithms for Vessel Tracking: A Review

arXiv: https://arxiv.org/abs/2012.08929 · HTML: https://mlsanity.com/paper/2012.08929/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography](https://mlsanity.com/paper/2204.08466/) (2022)
- [Curvilinear object segmentation in medical images based on ODoS filter and deep learning network](https://mlsanity.com/paper/2301.07475/) (2023)
- [Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation](https://mlsanity.com/paper/2305.17054/) (2023)
- [AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows](https://mlsanity.com/paper/2503.12515/) (2025)
- [Working memory inspired hierarchical video decomposition with transformative representations](https://mlsanity.com/paper/2204.10105/) (2022)
- [Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model](https://mlsanity.com/paper/2403.01362/) (2024)
- [KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation](https://mlsanity.com/paper/2410.02808/) (2024)
- [SeqSeg: Learning Local Segments for Automatic Vascular Model Construction](https://mlsanity.com/paper/2501.15712/) (2025)
- [DVasMesh: Deep Structured Mesh Reconstruction from Vascular Images for Dynamics Modeling of Vessels](https://mlsanity.com/paper/2412.00840/) (2024)

## Abstract (arXiv)

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.