|Title:||TGIF : topological gap in-fill for vascular networks|
|Authors :||Schneider, Matthias|
Menze, Bjoern H.
|Proceedings:||Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II|
|Conference details:||MICCAI, 17th International Conference, Boston, USA, 14-18 September 2014|
|Publisher / Ed. Institution :||Springer|
|Publisher / Ed. Institution:||Cham|
|License (according to publishing contract) :||Licence according to publishing contract|
|Series :||Lecture Notes in Computer Science|
|Type of review:||Peer review (Publication)|
|Subject (DDC) :||610: Medicine and health|
|Abstract:||This paper describes a new approach for the reconstruction of complete 3-D arterial trees from partially incomplete image data. We utilize a physiologically motivated simulation framework to iteratively generate artificial, yet physiologically meaningful, vasculatures for the correction of vascular connectivity. The generative approach is guided by a simplified angiogenesis model, while at the same time topological and morphological evidence extracted from the image data is considered to form functionally adequate tree models. We evaluate the effectiveness of our method on four synthetic datasets using different metrics to assess topological and functional differences. Our experiments show that the proposed generative approach is superior to state-of-the-art approaches that only consider topology for vessel reconstruction and performs consistently well across different problem sizes and topologies.|
|Departement:||Life Sciences und Facility Management|
|Organisational Unit:||Institute of Applied Simulation (IAS)|
|Publication type:||Conference Paper|
|Appears in Collections:||Publikationen Life Sciences und Facility Management|
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