2014
DOI: 10.1007/978-3-319-10470-6_12
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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 adeq… Show more

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Cited by 11 publications
(12 citation statements)
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“…To generate synthetic data, we follow the method of Schneider et al (2012) which implements a simulator of a vascular tree that follows a generative process inspired by the biology of angiogenesis. This approach, described in Schneider et al (2012), has initially been developed to complement missing elements of a vascular tree, a common problem in µCT imaging of the vascular bed (Schneider et al, 2014). We now use this generator to simulate physiologically plausible vascular trees that we can use in training our CNN algorithms.…”
Section: Synthetic Data For Transfer Learningmentioning
confidence: 99%
See 3 more Smart Citations
“…To generate synthetic data, we follow the method of Schneider et al (2012) which implements a simulator of a vascular tree that follows a generative process inspired by the biology of angiogenesis. This approach, described in Schneider et al (2012), has initially been developed to complement missing elements of a vascular tree, a common problem in µCT imaging of the vascular bed (Schneider et al, 2014). We now use this generator to simulate physiologically plausible vascular trees that we can use in training our CNN algorithms.…”
Section: Synthetic Data For Transfer Learningmentioning
confidence: 99%
“…However, assembling a properly labeled dataset of 3-D curvilinear structures, such as vessels and vessel features, takes a lot of human effort and time, which turns out to be the bottleneck for most medical applications. To overcome this problem, we generate synthetic data based on the method proposed in Schneider et al (2012Schneider et al ( , 2014. A brief description of this process has already been presented in section 2.3.…”
Section: Synthetic Datasetmentioning
confidence: 99%
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“…This high level strategy is complementary to lower level ones like in [11], where only local image features communicate to fill the gaps. In the same vein, the simultaneous reconstruction and separation of multiple interwoven tree structures using high-level representation of the trees was also proposed in [1], and a physiologically motivated strategy based on a simplified angiogenesis model was proposed in [12] to correct the vascular connectivity. Closer to our strategy, [3] derived intensity based information within a tensor model to perform the robust segmentation of tubular structures.…”
Section: Introductionmentioning
confidence: 99%