Automated Nomenclature of Upper Abdominal Arteries for Displaying Anatomical Names on Virtual Laparoscopic Images

Mori, Kensaku; Oda, Masahiro; Egusa, Tomohiko; Jiang, Zhengang; Kitasaka, Takayuki; Fujiwara, Michitaka; Misawa, Kazunari

doi:10.1007/978-3-642-15699-1_37

Cited by 13 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second variant is using the unary attributes and the graph preserving ordering, but without the prior term. This can be considered as an adaptation of the approaches of [24] and [31]. The last two variants correspond to the proposed method with two different prior terms.…”

Section: Evaluation Methodologymentioning

confidence: 98%

“…The variability in topology is either not considered or the appropriate reference model had to be initialized manually. Mori et al [31] tuned their approach to a specific task of labeling abdominal arteries, where many thin arteries branch from the thick ones. Each branch is modeled with multivariate Gaussian distribution which is then used to obtain label likelihood for a given test branch.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation

Bogunović

Pozo

Cárdenes

et al. 2013

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Anatomical labeling of the cerebral arteries forming the Circle of Willis (CoW) enables inter-subject comparison, which is required for geometric characterization and discovering risk factors associated with cerebrovascular pathologies. We present a method for automated anatomical labeling of the CoW by detecting its main bifurcations. The CoW is modeled as rooted attributed relational graph, with bifurcations as its vertices, whose attributes are characterized as points on a Riemannian manifold. The method is first trained on a set of pre-labeled examples, where it learns the variability of local bifurcation features as well as the variability in the topology. Then, the labeling of the target vasculature is obtained as maximum a posteriori probability (MAP) estimate where the likelihood of labeling individual bifurcations is regularized by the prior structural knowledge of the graph they span. The method was evaluated by cross-validation on 50 subjects, imaged with magnetic resonance angiography, and showed a mean detection accuracy of 95%. In addition, besides providing the MAP, the method can rank the labelings. The proposed method naturally handles anatomical structural variability and is demonstrated to be suitable for labeling arterial segments of the CoW.

show abstract

Section: Evaluation Methodologymentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation

Bogunović

Pozo

Cárdenes

et al. 2013

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…The second variant has the regularization by topology added (SVM+TP), and can be considered as an adaptation of [5]. Finally, the third variant is the proposed one, which includes the prior term (SVM+TP+AP), where the probability of appearance of a particular anatomical variability coming from the missing BoI is learned from the training set.…”

Section: Resultsmentioning

confidence: 99%

“…This makes the methods design for them difficult to apply to the task of labeling cerebral vessels where the vessels are in general longer and more curved and the bifurcations more complex. Recently, Mori et al [5] tuned their approach to a specific task of labeling abdominal arteries but even the authors themselves stated that the application to other organs would be challenging. Bogunović et al [6] showed that the ICA terminal bifurcation can be successfully classified using support vector machine (SVM) but only a single bifurcation is detected and no tree properties are considered.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Labeling of the Anterior Circulation of the Circle of Willis Using Maximum a Posteriori Classification

Bogunović

Pozo

Cárdenes

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Automated anatomical labeling of the arteries forming the Circle of Willis is of great interest as facilitates inter-subject comparison required to discover geometric risk factors for the development of vascular pathologies. In this paper, we present a method for anatomical labeling of vessels forming anterior part of the Circle of Willis by detecting the five main vessel bifurcations. The method is first trained on a set of pre-labeled examples, where it learns local bifurcation features as well as global variation in the anatomy of the extracted vascular trees. Then the labeling of the target vascular tree is formulated as maximum a posteriori solution where the classifications of individual bifurcations are regularized by the prior learned knowledge of the tree they span. The method was evaluated by cross-validation on 30 subjects, which showed the vascular trees were correctly anatomically labeled in 90% of cases. The proposed method can naturally handle anatomical variations and is shown to be suitable for labeling arterial segments of Circle of Willis.

show abstract

“…Subsequently, a depth-first search finds the global optimal assignment of branch names taking into account topological constraints. In another line of work, Mori et al [5] label the abdominal arteries, which they consider more difficult than labeling bronchi due to the larger variation. They state that their approach is expected not to work on vasculature of other organs.…”

Section: Introductionmentioning

confidence: 98%

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

Robben

Sunaert

Thijs

et al. 2013

Advanced Information Systems Engineering

View full text Add to dashboard Cite

Abstract.A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary programming problem which can be solved efficiently. Unlike previous methods, our approach can handle non tree-like vasculature and large topological differences. The method is evaluated in a leave-one-out test on MRA of 30 subjects where it achieves a sensitivity of 93% and a specificity of 85% with an average error of 1.5 mm on matching bifurcations in the vascular graph.

show abstract

Automated Nomenclature of Upper Abdominal Arteries for Displaying Anatomical Names on Virtual Laparoscopic Images

Cited by 13 publications

References 6 publications

Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation

Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation

Anatomical Labeling of the Anterior Circulation of the Circle of Willis Using Maximum a Posteriori Classification

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

Contact Info

Product

Resources

About