Simultaneous segmentation and anatomical labeling of the cerebral vasculature

Robben, David; Türetken, Engin; Sunaert, Stefan; Thijs, Vincent; Wilms, Guy; Fua, Pascal; Maes, Frederik; Suetens, Paul

doi:10.1016/j.media.2016.03.006

Cited by 55 publications

(52 citation statements)

References 20 publications

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“…Geodesic are integrated in a graph-based framework in [88], [115]. The main innovation is allowing cycles inside the graph, avoiding early-termination due to bifurcations or vessel kissing.…”

Section: B Minimum Cost Pathmentioning

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

469

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%

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

Moccia

Momi

Hadji

et al. 2018

Computer Methods and Programs in Biomedicine

469

263

View full text Add to dashboard Cite

show abstract

“…While we focussed on large grid-graphs that are most important for lowlevel segmentation and reconstruction, we expect that our findings transfer to MCCS problems and related ILP-based formulations on sparse graphs, e.g. those discussed in [1,2,3,4], and thus consider this a promising direction for future work. Besides, it will be intersting to investigate the effect of our propositions in the presence of higher-order terms.…”

Section: Discussionmentioning

confidence: 86%

“…While [1,2,3,4] successfully solve an MCCS problem on heavily preprocessed, application-specific, sparse graphs, it would also be interesting to enforce connectedness on both very dense or large grid-graphs, for example in low-level segmentation tasks (Fig. 1, left), for 3D/4D reconstruction problems (Fig.…”

Section: Introductionmentioning

confidence: 99%

The Minimum Cost Connected Subgraph Problem in Medical Image Analysis

Rempfler

Andres

Menze

2016

Lecture Notes in Computer Science

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Abstract. Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs. Examples include the reconstruction of neural or vascular structures under connectedness constraints. We discuss the minimum cost connected subgraph (MCCS) problem and its approximations from the perspective of medical applications. We propose a) objective-dependent constraints and b) novel constraint generation schemes to solve this optimization problem exactly by means of a branch-and-cut algorithm. These are shown to improve scalability and allow us to solve instances of two medical benchmark datasets to optimality for the first time. This enables us to perform a quantitative comparison between exact and approximative algorithms, where we identify the geodesic tree algorithm as an excellent alternative to exact inference on the examined datasets.

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“…(Fig. 1) with Accuracy (A), precision (P), recall (R) and Specificity (S) are reported for the proposed method with (Ours) and without (Ours w/o) the topological constraints, as well as for the best performing state-of-the-art methods [2,10]. Table 2 shows the accuracy, precision, recall and specificity for each bifurcation of interest separately, using our method with and without the topological constraints.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…It then combines the MAP with a graph matching method to label the CoW in the form of three separate trees. The MAP inferences in [9,10] are formulated as a quadratic binary programming problem. This formulation can handle non tree-like vasculature with high efficiency.…”

Section: Introductionmentioning

confidence: 99%

Automatic Labeling of Vascular Structures with Topological Constraints via HMM

Wang

Liu

Mou

et al. 2017

Lecture Notes in Computer Science

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Identification of anatomical vessel branches is a prerequisite task for diagnosis, treatment and inter-subject comparison. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our method first extracts bifurcations of interest from the centerlines of vessels, where a set of geometric features are also calculated from. Then the probability distribution of every bifurcation is learned using a XGBoost classifier. Finally a Hidden Markov Model with a restricted transition strategy is constructed in order to find the most likely labeling configuration of the whole structure, while also enforcing topological consistency. In this paper, the proposed approach has been evaluated through leave-one-out cross validation on 50 subjects of centerlines obtained from MRA images of healthy volunteers' Circle of Willis. Results demonstrate that our method can achieve higher accuracy and specificity, while obtaining similar precision and recall, when comparing to the best performing state-of-the-art methods. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment. Source code and data are available under

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Simultaneous segmentation and anatomical labeling of the cerebral vasculature

Cited by 55 publications

References 20 publications

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

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

The Minimum Cost Connected Subgraph Problem in Medical Image Analysis

Automatic Labeling of Vascular Structures with Topological Constraints via HMM

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