A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes

Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous...

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Bibliographic Details
Published in:Medical image analysis Vol. 13; no. 6; pp. 819 - 845
Main Authors: Lesage, David, Angelini, Elsa D., Bloch, Isabelle, Funka-Lea, Gareth
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.12.2009
Subjects:
3D angiography
Algorithms
Angiography - methods
Artificial Intelligence
Computed tomography angiography (CTA)
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Magnetic resonance angiography (MRA)
Pattern Recognition, Automated - methods
Reproducibility of Results
Review
Segmentation algorithms
Sensitivity and Specificity
Subtraction Technique
Vascular models
Vascular segmentation
Vessel-dedicated features
Vascular models
Computed tomography angiography (CTA)
Magnetic resonance angiography (MRA)
Segmentation algorithms
Vascular segmentation
Vessel-dedicated features
Review
3D angiography
ISSN:1361-8415, 1361-8423, 1361-8423
Online Access:Get full text
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Summary:Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task. Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2009.07.011