Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287990971> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W4287990971 abstract "Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology. However, they require water-tight surface meshes as inputs. Furthermore, adding vessels radii information on their graph compartments restricts their alignment along vascular centerlines. Here, we propose a novel graphing scheme that works with relaxed input requirements and intrinsically captures vessel radii information. The proposed approach is based on deforming geometric graphs constructed within vascular boundaries. Under a laplacian optimization framework, we assign affinity weights on the initial geometry that drives its iterative contraction toward vessels centerlines. We present a mechanism to decimate graph structure at each run and a convergence criterion to stop the process. A refinement technique is then introduced to obtain final vascular models. Our implementation is available on https://github.com/Damseh/VascularGraph. We benchmarked our results with that obtained using other efficient and stateof-the-art graphing schemes, validating on both synthetic and real angiograms acquired with different imaging modalities. The experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. Furthermore, it surpasses other techniques in providing representative models that capture all anatomical aspects of vascular structures." @default.
- W4287990971 created "2022-07-26" @default.
- W4287990971 creator A5025218490 @default.
- W4287990971 creator A5072102193 @default.
- W4287990971 creator A5072923919 @default.
- W4287990971 creator A5075040052 @default.
- W4287990971 creator A5079507190 @default.
- W4287990971 date "2019-12-20" @default.
- W4287990971 modified "2023-09-24" @default.
- W4287990971 title "Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks" @default.
- W4287990971 doi "https://doi.org/10.48550/arxiv.1912.10003" @default.
- W4287990971 hasPublicationYear "2019" @default.
- W4287990971 type Work @default.
- W4287990971 citedByCount "0" @default.
- W4287990971 crossrefType "posted-content" @default.
- W4287990971 hasAuthorship W4287990971A5025218490 @default.
- W4287990971 hasAuthorship W4287990971A5072102193 @default.
- W4287990971 hasAuthorship W4287990971A5072923919 @default.
- W4287990971 hasAuthorship W4287990971A5075040052 @default.
- W4287990971 hasAuthorship W4287990971A5079507190 @default.
- W4287990971 hasBestOaLocation W42879909711 @default.
- W4287990971 hasConcept C11413529 @default.
- W4287990971 hasConcept C114614502 @default.
- W4287990971 hasConcept C115178988 @default.
- W4287990971 hasConcept C121684516 @default.
- W4287990971 hasConcept C132525143 @default.
- W4287990971 hasConcept C134306372 @default.
- W4287990971 hasConcept C162324750 @default.
- W4287990971 hasConcept C165700671 @default.
- W4287990971 hasConcept C184720557 @default.
- W4287990971 hasConcept C2777303404 @default.
- W4287990971 hasConcept C31487907 @default.
- W4287990971 hasConcept C33923547 @default.
- W4287990971 hasConcept C41008148 @default.
- W4287990971 hasConcept C50522688 @default.
- W4287990971 hasConcept C80444323 @default.
- W4287990971 hasConceptScore W4287990971C11413529 @default.
- W4287990971 hasConceptScore W4287990971C114614502 @default.
- W4287990971 hasConceptScore W4287990971C115178988 @default.
- W4287990971 hasConceptScore W4287990971C121684516 @default.
- W4287990971 hasConceptScore W4287990971C132525143 @default.
- W4287990971 hasConceptScore W4287990971C134306372 @default.
- W4287990971 hasConceptScore W4287990971C162324750 @default.
- W4287990971 hasConceptScore W4287990971C165700671 @default.
- W4287990971 hasConceptScore W4287990971C184720557 @default.
- W4287990971 hasConceptScore W4287990971C2777303404 @default.
- W4287990971 hasConceptScore W4287990971C31487907 @default.
- W4287990971 hasConceptScore W4287990971C33923547 @default.
- W4287990971 hasConceptScore W4287990971C41008148 @default.
- W4287990971 hasConceptScore W4287990971C50522688 @default.
- W4287990971 hasConceptScore W4287990971C80444323 @default.
- W4287990971 hasLocation W42879909711 @default.
- W4287990971 hasOpenAccess W4287990971 @default.
- W4287990971 hasPrimaryLocation W42879909711 @default.
- W4287990971 hasRelatedWork W1981840870 @default.
- W4287990971 hasRelatedWork W2001557365 @default.
- W4287990971 hasRelatedWork W2142326458 @default.
- W4287990971 hasRelatedWork W2298820818 @default.
- W4287990971 hasRelatedWork W2389786622 @default.
- W4287990971 hasRelatedWork W2950566150 @default.
- W4287990971 hasRelatedWork W2951820272 @default.
- W4287990971 hasRelatedWork W3083272131 @default.
- W4287990971 hasRelatedWork W4310911903 @default.
- W4287990971 hasRelatedWork W2624039590 @default.
- W4287990971 isParatext "false" @default.
- W4287990971 isRetracted "false" @default.
- W4287990971 workType "article" @default.