Reconstructing cerebrovascular networks under local physiological constraints by integer programming

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

doi:10.1016/j.media.2015.03.008

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

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

^*Corresponding author for this work

Cell & Developmental Biology

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.

Original language	English (US)
Pages (from-to)	86-94
Number of pages	9
Journal	Medical Image Analysis
Volume	25
Issue number	1
DOIs	https://doi.org/10.1016/j.media.2015.03.008
State	Published - Oct 1 2015

Funding

This research was supported by the Technische Universität München – Institute for Advanced Study (funded by the German Excellence Initiative and the European Union Seventh Framework Programme under grant agreement n 291763 , the Marie Curie COFUND program of the European Union ), by grants from the EMDO foundation , Swiss National Science Foundation grant 136822 , and the Swiss National Center of Competence in Research on Computer Aided and Image Guided Medical Interventions (NCCR Co-Me) supported by the Swiss National Science Foundation . Use of the Advanced Photon Source was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences , under Contract No. DE-AC02-06CH11357 .

Keywords

Cerebrovascular networks
Integer programming
Vascular network extraction
Vessel segmentation
Vessel tracking

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Health Informatics
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

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10.1016/j.media.2015.03.008

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title = "Reconstructing cerebrovascular networks under local physiological constraints by integer programming",

abstract = "We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.",

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AU - Rempfler, Markus

AU - Schneider, Matthias

AU - Ielacqua, Giovanna D.

AU - Xiao, Xianghui

AU - Stock, Stuart R.

AU - Klohs, Jan

AU - Székely, Gábor

AU - Andres, Bjoern

AU - Menze, Bjoern H.

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Y1 - 2015/10/1

N2 - We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.

AB - We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.

KW - Cerebrovascular networks

KW - Integer programming

KW - Vascular network extraction

KW - Vessel segmentation

KW - Vessel tracking

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Reconstructing cerebrovascular networks under local physiological constraints by integer programming

Abstract

Funding

Keywords

ASJC Scopus subject areas

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