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Updated: Nov 18, 2025

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Learning-based algorithms for vessel tracking: A review.

Dengqiang Jia¹, Xiahai Zhuang²

¹School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|February 6, 2021

Summary

This summary is machine-generated.

This review covers machine learning methods for vessel tracking, essential for diagnosing vascular diseases. It examines conventional and deep learning approaches, highlighting evaluation challenges and future research directions.

Keywords:

Learning-based algorithms Review Vessel tracking

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Area of Science:

Medical Imaging
Computational Biology
Artificial Intelligence

Background:

Vessel tracking is vital for diagnosing and treating vascular diseases using medical imaging.
Challenges in vessel tracking stem from complex vessel shapes and angiography image properties.
Existing methods often struggle with accurate recognition tasks like seed point detection, centerline extraction, and segmentation.

Purpose of the Study:

To provide a comprehensive literature review of vessel-tracking algorithms.
To focus specifically on machine learning-based approaches for vessel tracking.
To identify current challenges and future research opportunities in the field.

Main Methods:

Review of conventional machine learning algorithms for vessel tracking.
Survey of deep learning frameworks applied to vessel tracking.
Analysis of evaluation metrics and methodologies for vessel-tracking algorithms.

Main Results:

Conventional machine learning methods offer foundational approaches to vessel tracking.
Deep learning frameworks demonstrate significant advancements in accuracy and efficiency.
Standardized evaluation remains a critical issue across different methods.

Conclusions:

Machine learning, particularly deep learning, shows great promise for improving vessel-tracking accuracy.
Further research is needed to address remaining challenges in robustness and generalizability.
Standardized evaluation protocols are essential for comparing and advancing vessel-tracking techniques.