Key result
Learning-based algorithms, including conventional machine learning and deep learning frameworks, provide diverse approaches for vessel tracking tasks such as centerline extraction and segmentation.
Why the study?
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases.
This review summarizes the state-of-the-art machine learning and deep learning techniques for vessel tracking in medical imaging, highlighting their applications and evaluation metrics.
Answers are grounded in this paper and cite their sources.
Vessel-tracking advances may aid angiography diagnostics; leaves open standardized clinical validation and outcome trials.
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.
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Jia et al. (2020) conducted a review in Vascular diseases. Learning-based algorithms was evaluated. Learning-based algorithms, including conventional machine learning and deep learning frameworks, provide diverse approaches for vessel tracking tasks such as centerline extraction and segmentation.
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