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Conference Papers Year : 2017

Robust Automatic Graph-based Skeletonization of Hepatic Vascular Trees

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Abstract

The topologies of vascular trees embedded inside soft tissues carry important information which can be successfully exploited in the context of the computer-assisted planning and navigation. For example, topological matching of complete and/or partial hepatic trees provides important source of correspondences that can be employed straightforwardly by image registration algorithms. Therefore, robust and reliable extraction of vascular topologies from both pre-and intra-operative medical images is an important task performed in the context of surgical planning and navigation. In this paper, we propose an extension of an existing graph-based method where the vascular topology is constructed by computation of shortest paths in a minimum-cost spanning tree obtained from binary mask of the vascularization. We suppose that the binary mask is extracted from a 3D CT image using automatic segmentation and thus suffers from important artefacts and noise. When compared to the original algorithm, the proposed method (i) employs a new weight-ing measure which results in smoothing of extracted topology and (ii) introduces a set of tests based on various geometric criteria which are executed in order to detect and remove spurious branches. The method is evaluated on vascular trees extracted from abdominal contrast-enhanced CT scans and MR images. The method is quantitatively compared to the original version of the algorithm showing the importance of proposed modifications. Since the branch testing depends on parameters, the para-metric study of the proposed method is presented in order to identify the optimal parametrization.
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Dates and versions

hal-01576771 , version 1 (25-08-2017)

Identifiers

  • HAL Id : hal-01576771 , version 1

Cite

Rosalie Plantefève, Samuel Kadoury, An Tang, Igor Peterlik. Robust Automatic Graph-based Skeletonization of Hepatic Vascular Trees. CIIV STENT 2017, Sep 2017, Québec, Canada. ⟨hal-01576771⟩

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