Segmentation of lung vessel trees by global optimization

Bruyninckx, Pieter; Loeckx, Dirk; Vandermeulen, Dirk; Suetens, Paul

doi:10.1117/12.811570

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…In [25], [24] ant colony optimization, a population-based metaheuristic used to find approximate solutions to optimization problems, is used to segment both lung and liver vessels. The method connects vessel bifurcation by cost path algorithm and uses the ant colony optimization method to retrieve the optimal vessel tree between all possible paths.…”

Section: A Unsupervisedmentioning

confidence: 99%

“…Feng et al [20] 2010 Brain MRA Unsupervised machine learning Hassouna et al [21] 2006 Brain MRA (Sec. V-A) Oliveira et al [22] 2011 Liver CT Goceri et al [23] 2017 Liver MRI Bruyninckx et al [24] 2010 Liver CT Bruyninckx et al [25] 2009 Lung CT Asad et al [26] 2017 Retina CFP Mapayi et al [27] 2015 Retina CFP Sreejini et al [28] 2015 Retina CFP Cinsdikici et al [29] 2009 Retina CFP Al-Rawi et al [30] 2007 Retina CFP Hanaoka et al [31] 2015 Brain MRA Supervised machine learning Sironi et al [32] 2014 Brain Microscopy (Sec. V-B) Merkow et al [33] 2016 Cardiovascular and Lung CT and MRI Sankaran et al [34] 2016 Coronary CTA Schaap et al [35] 2011 Coronary CTA Zheng et al [36] 2011 Coronary CT Nekovei et al [37] 1995 Coronary CT Smistad et al [38] 2016 Femoral region, Carotid US Chu et al [39] 2016 Liver X-ray fluoroscopic Orlando et al [40] 2017 Retina CFP Dasgupta et al [41] 2017 Retina CFP Mo et al [42] 2017 Retina CFP Lahiri et al [43] 2017 Retina CFP Annunziata et al [44] 2016 Retina Microscopy Fu et al [45] 2016 Retina CFP Luo et al [46] 2016 Retina CFP Liskowski et al [47] 2016 Retina CFP Li et al [48] 2016 Retina CFP Javidi et al [49] 2016 Retina CFP Maninis et al [50] 2016 Retina CFP Prentasvic et al [51] 2016 Retina CT Wu et al [52] 2016 Retina CFP Annunziata et al [53] 2015 Retina Microscopy Annunziata et al [54] 2015 Retina Microscopy Vega et al [55] 2015 Retina CFP Wang et al …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

471

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: A Unsupervisedmentioning

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

471

263

View full text Add to dashboard Cite

show abstract

“…Methods used in the processing and quantification of neuronal network image datasets may yield useful techniques for analyzing microvascular networks . In vivo clinical imaging techniques, such as micro‐CT of lung vascular networks, may also yield insight into extending 2D imaging and quantification into the third dimension …”

Section: Microvascular Network Analysis and Quantificationmentioning

confidence: 99%

Methods to label, image, and analyze the complex structural architectures of microvascular networks

et al. 2019

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Microvascular networks play key roles in oxygen transport and nutrient delivery to meet the varied and dynamic metabolic needs of different tissues throughout the body, and their spatial architectures of interconnected blood vessel segments are highly complex. Moreover, functional adaptations of the microcirculation enabled by structural adaptations in microvascular network architecture are required for development, wound healing, and often invoked in disease conditions, including the top eight causes of death in the Unites States. Effective characterization of microvascular network architectures is not only limited by the available techniques to visualize microvessels but also reliant on the available quantitative metrics that accurately delineate between spatial patterns in altered networks. In this review, we survey models used for studying the microvasculature, methods to label and image microvessels, and the metrics and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, yet we estimate that they have collectively attained low adoption rates, possibly due to limitations with basic validation, segmentation performance, and nonstandard sets of quantification metrics. To address these existing constraints, we discuss opportunities to improve effectiveness, rigor, and reproducibility of microvascular network quantification to better serve the current and future needs of microvascular research.

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“…The most similar previous work to ours is that of Bruyninckx et al [3], as briefly mentioned in Section 1.1, where a two-stage approach and basic physiology principles are also adopted. For the first stage, a set of branching/bifurcation joint points instead of vessel centerlines are detected, and during the second stage, the connection problem is formulated to be a Steiner minimal tree problem, where the weight (cost) is obtained from both image intensity and a couple of physiology principles: Murray's law and the minimum volume principle.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Reconstructing the full vascular network was not discussed in [20] either. Bruynickx et al [3] is, to our knowledge, among the first to integrate the physiology principle of minimum volume into vessel segmentation. However the geometry of vessel segments is extremely simplified in their work, and the outcome also heavily depends on the prior detection of the branching/bifurcation joints, which was found to be unreliable – large part of the vascular network was missing in the final results.…”

Section: Introductionmentioning

confidence: 99%

Vascular tree reconstruction by minimizing a physiological functional cost

Jiang

Zhuang

Sinusas

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.

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Segmentation of lung vessel trees by global optimization

Cited by 7 publications

References 2 publications

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

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

Methods to label, image, and analyze the complex structural architectures of microvascular networks

Vascular tree reconstruction by minimizing a physiological functional cost

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