Semiautomatic carotid lumen segmentation for quantification of lumen geometry in multispectral MRI

Tang, Hui; Walsum, Theo van; Onkelen, Robbert S. van; Hameeteman, Reinhard; Klein, Stefan; Schaap, Michiel; Tori, Fufa L.; Bouwhuijsen, Quirijn J.A. van den; Witteman, Jacqueline C. M.; Lugt, Aad van der; Vliet, Lucas J. van; Niessen, Wiro J.

doi:10.1016/j.media.2012.05.014

Cited by 48 publications

(55 citation statements)

References 41 publications

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“…In [101], carotids are segmented in multispectral MRI. The MCP between two manually defined seed points (a start and an end point) is found accordingly to a metric based on image gradient [202] and intensity [203].…”

Section: B Minimum Cost Pathmentioning

confidence: 99%

“…VI-A) Law et al [71] 2009 Brain & Coronary MRA & CTA Moreno et al [72] 2013 Coronary CTA Wang et al [73] 2012 Coronary CTA Cheng et al [74] 2015 Carotid,Coronary Liver, & Lung Zhu et al [75] 2009 Lung CTA Zhang et al [76] 2015 Retina CFP Patwardhan et al [77] 2012 --US [80] 2007 Brain MRA Wang et al [81] 2009 Carotid US Liang et al [82] 2015 Liver Microscopy Zhao et al [83] 2015 Retina CFP & FA Zhao et al [84] 2015 Retina CFP Wang et al [85] 2015 Retina CFP Xiao et al [86] 2013 Retina CFP Law et al [87] 2006 Retina CFP Robben et al [88] 2016 Brain MRA Tracking approaches Rempfler et al [89] 2015 Brain MRA (Sec. VII) Yureidini et al [90] 2012 Brain 3DRA Cetin et al [91] 2015 Brain MRA Coronary CTA Cetin et al [92] 2013 Brain MRA Coronary CTA Shim et al [93] 2006 Brain CTA Cherry et al [94] 2015 Colon CTA Shin et al [95] 2016 Coronary FA Carrillo et al [96] 2007 Carotid, aorto-iliac MRA Coronary, pulmonary arteries CTA Amir-Khalili et al [97] 2015 Carotid US Benmansour et al [98] 2011 Carotid CTA Biesdorf et al [99] 2015 Coronary CTA Lugauer et al [100] 2014 Coronary CTA Tang et al [101] 2012 Coronary MR Wang et al [102] 2012 Coronary CTA Friman et al [103] 2010 Coronary & CTA Liver Li et al [104] 2009 Coronary CTA Wink et al [105] 2002 Coronary MRA Zeng et al [106] 2017 Liver CTA Bauer et al [107] 2010 Liver CT Amir-Khalili et al [108] 2015 Kidney Endoscopy images Amir-Khalili et al …”

Section: Introductionmentioning

confidence: 99%

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Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

423

263

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Abstract-Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semiautomatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).Objective: This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.Method: This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.Discussion: Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. Conclusion:No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.

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Section: B Minimum Cost Pathmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

423

263

View full text Add to dashboard Cite

show abstract

“…An initial segmentation of the lumen was obtained using the method proposed by Tang et al [11]. In this method first the lumen centerlines are determined by finding a minimum cost path between three user-defined seed points in the common, internal, and external carotid arteries.…”

Section: Initial Segmentationmentioning

confidence: 99%

Carotid Artery Wall Segmentation by Coupled Surface Graph Cuts

Arias-Lorza

Petersen

Engelen

et al. 2013

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. We present a three-dimensional coupled surface graph cut algorithm for carotid wall segmentation from Magnetic Resonance Imaging (MRI). Using cost functions that highlight both inner and outer vessel wall borders, the method combines the search for both borders into a single graph cut. Our approach requires little user interaction and can robustly segment the bifurcation section of the carotid artery. Experiments on 32 carotid arteries from 16 patients show good agreement between manual segmentation performed by an expert and our method. The mean relative area of overlap is more than 85% for the lumen and outer vessel wall. In addition, differences in measured wall thickness with respect to the manual annotations were smaller than the image pixel size.

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“…Accordingly, significant efforts have been placed on the development of efficient computer-aided methods for vessel wall extraction. In previous work, approaches for automatic segmentation of carotid and aorta employ classical deformable models (eg, active contour model, 15 active shape model, 16 discrete dynamic contour model, 17 ellipse model, 18 cylinder model; 19,20 deformable model implemented using levelset; [21][22][23] graph-cuts; 24,25 or intensity probability density function matching. 26 In these approaches, some reported methods 19,22,23 demonstrated only the segmentation of inner border, while other methods [15][16][17][18]20,21,[24][25][26] are able to segment both inner and outer vessel walls.…”

mentioning

confidence: 99%

“…In general, the requirements range from one interaction for every slice [15][16][17][18][19]24 to one single interaction to start the complete segmentation. [20][21][22][23]25,26 Additionally, the methods presented are optimized to a specific type of vessel (eg, carotid [15][16][17][18][19][20]23,25,26 or aorta 21,22,24 ) and no generally applicable method has been described. While semiautomated segmentation methods are generally less time-consuming and more reproducible compared to fully manual delineation, they still remain labor-intensive and therefore are not optimally suited for cohort studies where large volumes of data need to be processed.…”

mentioning

confidence: 99%

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline

Gao

Klooster

Brandts

et al. 2016

J. Magn. Reson. Imaging

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Semiautomatic carotid lumen segmentation for quantification of lumen geometry in multispectral MRI

Cited by 48 publications

References 41 publications

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Carotid Artery Wall Segmentation by Coupled Surface Graph Cuts

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline

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