Extracting Vascular Networks under Physiological Constraints via Integer Programming

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; Xiao, Xianghui; Stock, Stuart R.; Klohs, Jan; Székely, Gábor; Andres, Bjoern; Menze, Bjoern

doi:10.1007/978-3-319-10470-6_63

Cited by 8 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed segmentation framework hence allows to fully automatically learn RF models for 3-D vessel segmentation on new datasets. For instance, in our recent study (Rempfler et al, 2014) we found the proposed approach to generalize well for different types of 3-D angiographic whole brain datasets acquired by micro magnetic resonance angiography (µMRA) or micro computed tomography (µCT) at different spatial resolution.…”

Section: Discussionmentioning

confidence: 92%

“…Similarly, elaborate break criteria are required to prevent the segmentation from leaking into the background, particularly for data with a low signal to noise ratio. Another group of methods follows the bottom-up paradigm aiming at optimizing a global neighborhood graph in order to incorporate spatial context (Türetken et al, 2012;Rempfler et al, 2014). The graph is commonly defined on the voxel locations based on a likelihood for a voxel belonging to a tubular structure as well as certain constraints for better robustness, e.g., handling of bifurcations and low-contrast regions (Breitenreicher et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

Schneider

Hirsch

Weber

et al. 2015

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

Contributions We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. Experiments We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Results Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. AbstractContributions. We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations.Experiments. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

Schneider

Hirsch

Weber

et al. 2015

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…The different values can be found in Table 2. The next table contains all the remaining parameters occurring in the 3D-1D coupled PDE based models (7), (6) and (11), (10). It can be seen at a first glance that most of the parameters are fixed apart from m ox ð Þ 0 and γ.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Most algorithms are concentrated on vessel detection and segmentation, while the determination of the vascular connectivity is neglected. 11 Further problems are associated with the image recording (image noise and artifacts) and sample preparation (air bubbles and clotting). 12 Motivated by such problems, different approaches for generating microvascular networks have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, an accurate reconstruction of vascular networks is still challenging, in particular, if fine‐scale microvascular networks are considered. Most algorithms are concentrated on vessel detection and segmentation, while the determination of the vascular connectivity is neglected 11 . Further problems are associated with the image recording (image noise and artifacts) and sample preparation (air bubbles and clotting) 12 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

Köppl¹,

Vidotto²,

Wohlmuth

2020

Numer Methods Biomed Eng

View full text Add to dashboard Cite

In this work, we introduce an algorithmic approach to generate microvascular networks starting from larger vessels that can be reconstructed without noticeable segmentation errors. Contrary to larger vessels, the reconstruction of fine-scale components of microvascular networks shows significant segmentation errors, and an accurate mapping is time and cost intense. Thus there is a need for fast and reliable reconstruction algorithms yielding surrogate networks having similar stochastic properties as the original ones. The microvascular networks are constructed in a marching way by adding vessels to the outlets of the vascular tree from the previous step. To optimise the structure of the vascular trees, we use Murray's law to determine the radii of the vessels and bifurcation angles. In each step, we compute the local gradient of the partial pressure of oxygen and adapt the orientation of the new vessels to this gradient. At the same time, we use the partial pressure of oxygen to check whether the considered tissue block is supplied sufficiently with oxygen. Computing the partial pressure of oxygen, we use a 3D-1D coupled model for blood flow and oxygen transport. To decrease the complexity of a fully coupled 3D model, we reduce the blood vessel network to a 1D graph structure and use a bi-directional coupling with the tissue which is described by a 3D homogeneous porous medium. The resulting surrogate networks are analysed with respect to morphological and physiological aspects. K E Y W O R D S 3D-1D coupled flow models, blood flow simulations, dimensionally reduced models, flows in porous media, oxygen transport, vascular growth

show abstract

TGIF: Topological Gap In-Fill for Vascular Networks

Schneider

Hirsch

Weber

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Extracting Vascular Networks under Physiological Constraints via Integer Programming

Cited by 8 publications

References 15 publications

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters

A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

TGIF: Topological Gap In-Fill for Vascular Networks

Contact Info

Product

Resources

About