Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Graph Matching

Robben, David; Sunaert, Stefan; Thijs, Vincent; Wilms, Guy; Maes, Frederik; Suetens, Paul

doi:10.1007/978-3-642-40811-3_71

Cited by 8 publications

(5 citation statements)

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“…Table 2a gives the precision and recall values over the evaluated images for the two measures. The overlap values of the two methods are relatively similar but still significantly better than thresholding and thinning, as used in [6] (precision: 73 (7), recall 64(6)). In terms of the topology criterion, our approach clearly outperforms that of [7], which is also illustrated in the close-ups of Fig.…”

Section: Simultaneous Segmentation and Labelingmentioning

confidence: 89%

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Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature

Robben

Türetken

Sunaert

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. The method first constructs an overcomplete graph capturing the vasculature. It then selects and labels the subset of edges that most likely represents the true vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we jointly optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This results in an Integer Program (IP), which we solve optimally using a branch-and-cut algorithm. We evaluate our approach on a public dataset of 50 cerebral MRA images, and demonstrate that it compares favorably against state-of-the-art methods.

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Section: Simultaneous Segmentation and Labelingmentioning

confidence: 89%

“…For example, Robben et al [6] label the CoW by matching bifurcations in this graph to a probabilistic atlas, taking into account both unary potentials of the bifurcations and also pairwise potentials between them. Bogunović et al [1] also label the CoW by matching bifurcations to an atlas.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature

Robben

Türetken

Sunaert

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

Self Cite

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“…Van de Giessen, M., Janssen, J.P., Brouwer, P.A., Reiber, J.H.C., Lelieveldt, B.P.F, Dijkstra, J. [2015] as well as Robben et al [2013], Bogunović et al [2013] worked with TOF-MRI data while Dunås et al [2016Dunås et al [ , 2017 worked with 4D-flow MRI data. Both works were made possible by using an atlas which serves as guidance for the classification of each vessel section, achieving sensitivities between 90% and 100%.…”

Section: Labelingmentioning

confidence: 99%

An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes

Thamm¹,

Jürgens²,

Taubmann³

et al. 2022

Preprint

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During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. In previous work we proposed VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedure, as in the worst case whole hemispheres may not be present due to impeded perfusion. Hence, we extended the labeling mechanism for the cerebral arteries to identify occluded vessels. In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92 % and 95 %. To the best of our knowledge, ours is the first work to address labeling and occlusion detection at once, whereby a sensitivity of 67 % and a specificity of 81 % were obtained for the latter. VirtualDSA++ also automatically segments and models the intracranial system, which we further used in a deep learning driven follow up work. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features. Exemplary, we derive in detail, firstly, the interactive planning of vascular interventions like the mechanical thrombectomy and secondly, the interactive suppression of vessel structures that are not of interest in diagnosing strokes (like veins). We discuss both features as well as further possibilities emerging from the proposed concept.

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“…The Manual anatomical labeling is a tedious and time consuming task. Therefore, its automation becomes crucial to streamline the geometric characterization of a large number of cases [10].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we are interested in the automatic labeling of major CoW bifurcations (Fig. 1(b)), where 90% of all the cerebral aneurysms occur [10].…”

Section: Introductionmentioning

confidence: 99%