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Damseh R, Pouliot P, Gagnon L, Sakadzic S, Boas D, Cheriet F, Lesage F. Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy. IEEE J Biomed Health Inform 2019; 23:2551-2562. [PMID: 30507542 PMCID: PMC6546554 DOI: 10.1109/jbhi.2018.2884678] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 μm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.
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Affiliation(s)
- Rafat Damseh
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Philippe Pouliot
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
| | - Louis Gagnon
- Physics Department, Université Laval, Quebec, QC, Canada
| | - Sava Sakadzic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - David Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Farida Cheriet
- Department of Computer and Software Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
| | - Frederic Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Department of Electrical Engineering, École Polytechnique de Montréal, Montreal, QC, Canada
- Research Centre, Montreal Hearth Institute, Montreal, QC, Canada
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Damseh R, Cheriet F, Lesage F. Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:661-665. [PMID: 30440483 DOI: 10.1109/embc.2018.8512285] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Segmentation of microvessels measured using two-photon microscopy has been studied in the literature with limited success due to uneven intensities associated with optical imaging and shadowing effects. In this work, we address this problem using a customized version of a recently developed fully convolutional neural network, namely, FC-DensNets. To train and validate the network, manual annotations of 8 angiograms from two-photon microscopy was used. Segmentation results are then compared with that of a state-of-the-art scheme that was developed for the same purpose and also based on deep learning. Experimental results show improved performance of used FC-DenseNet in providing accurate and yet end-to-end segmentation of microvessels in two-photon microscopy.
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Vigneshwaran V, Sands GB, LeGrice IJ, Smaill BH, Smith NP. Reconstruction of coronary circulation networks: A review of methods. Microcirculation 2019; 26:e12542. [DOI: 10.1111/micc.12542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/25/2019] [Accepted: 02/27/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Vibujithan Vigneshwaran
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
| | - Gregory B. Sands
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Ian J. LeGrice
- Department of Physiology University of Auckland Auckland New Zealand
| | - Bruce H. Smaill
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Nicolas P. Smith
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
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Blood vessel modeling for interactive simulation of interventional neuroradiology procedures. Med Image Anal 2017; 35:685-698. [DOI: 10.1016/j.media.2016.10.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Revised: 10/03/2016] [Accepted: 10/08/2016] [Indexed: 11/19/2022]
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Jochimsen TH, Zeisig V, Schulz J, Werner P, Patt M, Patt J, Dreyer AY, Boltze J, Barthel H, Sabri O, Sattler B. Fully automated calculation of image-derived input function in simultaneous PET/MRI in a sheep model. EJNMMI Phys 2016; 3:2. [PMID: 26872658 PMCID: PMC4752572 DOI: 10.1186/s40658-016-0139-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 01/29/2016] [Indexed: 12/04/2022] Open
Abstract
Background Obtaining the arterial input function (AIF) from image data in dynamic positron emission tomography (PET) examinations is a non-invasive alternative to arterial blood sampling. In simultaneous PET/magnetic resonance imaging (PET/MRI), high-resolution MRI angiographies can be used to define major arteries for correction of partial-volume effects (PVE) and point spread function (PSF) response in the PET data. The present study describes a fully automated method to obtain the image-derived input function (IDIF) in PET/MRI. Results are compared to those obtained by arterial blood sampling. Methods To segment the trunk of the major arteries in the neck, a high-resolution time-of-flight MRI angiography was postprocessed by a vessel-enhancement filter based on the inertia tensor. Together with the measured PSF of the PET subsystem, the arterial mask was used for geometrical deconvolution, yielding the time-resolved activity concentration averaged over a major artery. The method was compared to manual arterial blood sampling at the hind leg of 21 sheep (animal stroke model) during measurement of blood flow with O15-water. Absolute quantification of activity concentration was compared after bolus passage during steady state, i.e., between 2.5- and 5-min post injection. Cerebral blood flow (CBF) values from blood sampling and IDIF were also compared. Results The cross-calibration factor obtained by comparing activity concentrations in blood samples and IDIF during steady state is 0.98 ± 0.10. In all examinations, the IDIF provided a much earlier and sharper bolus peak than in the time course of activity concentration obtained by arterial blood sampling. CBF using the IDIF was 22 % higher than CBF obtained by using the AIF yielded by blood sampling. Conclusions The small deviation between arterial blood sampling and IDIF during steady state indicates that correction of PVE and PSF is possible with the method presented. The differences in bolus dynamics and, hence, CBF values can be explained by the different sampling locations (hind leg vs. major neck arteries) with differences in delay/dispersion. It will be the topic of further work to test the method on humans with the perspective of replacing invasive blood sampling by an IDIF using simultaneous PET/MRI.
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Affiliation(s)
- Thies H Jochimsen
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany.
| | - Vilia Zeisig
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Jessica Schulz
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, Leipzig, D-04103, Germany
| | - Peter Werner
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Marianne Patt
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Jörg Patt
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Antje Y Dreyer
- Fraunhofer Institute of Cell Therapy and Immunology, Perlickstr. 1, Leipzig, D-04103, Germany.,Translational Centre for Regenerative Medicine, University Leipzig, Philipp-Rosenthal-Str. 55, Leipzig, D-04103, Germany
| | - Johannes Boltze
- Fraunhofer Institute of Cell Therapy and Immunology, Perlickstr. 1, Leipzig, D-04103, Germany.,Translational Centre for Regenerative Medicine, University Leipzig, Philipp-Rosenthal-Str. 55, Leipzig, D-04103, Germany.,Fraunhofer Research Institution of Marine Biotechnology and Institute for Medical and Marine Biotechnology, University of Lübeck, Lübeck, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Bernhard Sattler
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
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Moreno R, Smedby Ö. Gradient-based enhancement of tubular structures in medical images. Med Image Anal 2015; 26:19-29. [DOI: 10.1016/j.media.2015.07.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Revised: 05/18/2015] [Accepted: 07/06/2015] [Indexed: 10/23/2022]
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Zuluaga MA, Orkisz M, Dong P, Pacureanu A, Gouttenoire PJ, Peyrin F. Bone canalicular network segmentation in 3D nano-CT images through geodesic voting and image tessellation. Phys Med Biol 2014; 59:2155-71. [DOI: 10.1088/0031-9155/59/9/2155] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Zhang H, Kheyfets VO, Finol EA. Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification. Med Eng Phys 2013; 35:1358-67. [PMID: 23608300 DOI: 10.1016/j.medengphy.2013.03.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2012] [Revised: 02/04/2013] [Accepted: 03/12/2013] [Indexed: 11/24/2022]
Abstract
The objective of this work is to develop a robust method for human abdominal aortic aneurysm (AAA) centerline detection that can contribute to the accurate computation of features for the prediction of AAA rupture risk. A semiautomatic algorithm is proposed for detecting the lumen centerline in contrast-enhanced abdominal computed tomography images based on online adaboost classifiers, which does not require prior image segmentation. The algorithm was developed and applied to thirty ruptured and thirty unruptured AAA image data sets and the tortuosities of the detected centerline were measured to assess the correlation between AAA tortuosity and the binary ruptured and unruptured labels. The lumen of each data set was segmented manually by a trained radiologist and the resulting centerlines of each data set were defined as the gold standard to evaluate the accuracy of the algorithm and to compare it against two widely used segmentation techniques. The average mean relative accuracy of the offline adaboost classifier is 91.9% with a standard deviation of 1.6%; for the online adaboost classifier it is 93.6% with a standard deviation of 1.9% (p<0.05). The online adaboost classifier outperforms the offline adaboost classifier while their computational costs are similar. Aneurysm tortuosity computed from an accurately derived lumen centerline using online adaboost is statistically higher for ruptured aneurysms compared to unruptured aneurysms, indicating that tortuosity can be used to assess rupture risk in the vascular clinic.
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Affiliation(s)
- Hong Zhang
- Institute for Complex Engineered Systems, Carnegie Mellon University, Pittsburgh, PA, USA
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Angiographic Image Analysis. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/978-1-4419-9779-1_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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Teng PY, Bagci AM, Alperin N. Automated prescription of an optimal imaging plane for measurement of cerebral blood flow by phase contrast magnetic resonance imaging. IEEE Trans Biomed Eng 2011; 58:2566-73. [PMID: 21672671 DOI: 10.1109/tbme.2011.2159383] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study describes and evaluates a semiautomated method for prescribing an optimal imaging plane that is located as close as possible to the skull base, and is simultaneously nearly perpendicular to the four arteries leading blood to the brain [internal carotid arteries (ICAs) and vertebral arteries (VAs)]. Such a method will streamline and improve reliability of the measurement of total cerebral blood flow and intracranial pressure by velocity encoding phase-contrast magnetic resonance imaging. The method first extracts the vessels' centerline from a 2-D time-of-flight magnetic resonance angiogram of the neck by performing distance transformations. An anatomical marker, the V2 segment of the VAs, is then identified to guide the imaging plane to be as close and below the skull base. An imaging plane that is nearly perpendicular to the ICAs and V2 segment of VAs is then identified by minimizing a misalignment value, estimated by a weighted mean of the angles between the plane's normal and the vessel axes at the vessel-plane intersections. The performance of the semiautomated method was evaluated by comparing manually selected planes to those found semiautomatically in nine magnetic resonance angiogram datasets. The semiautomated method consistently outperformed manual prescription with a significantly smaller misalignment value, 8.6° versus 20.7° (P < 0.001), respectively, and significantly improved reproducibility.
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Affiliation(s)
- Pang-yu Teng
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60612, USA.
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Chen K, Zhang Y, Pohl K, Syeda-Mahmood T, Song Z, Wong STC. Coronary artery segmentation using geometric moments based tracking and snake-driven refinement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:3133-7. [PMID: 21096589 PMCID: PMC3089772 DOI: 10.1109/iembs.2010.5627192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries. In this method, a new line direction based on first and second order geometric moments is adopted while both gradient and intensity information are used in the snake model to improve the accuracy. The approach has been evaluated on synthetic images as well as 8 clinical coronary CTA images with 32 coronary arteries. Our method achieves 94.7% overlap tracking ability within an average distance inside the vessel of 0.36 mm.
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Affiliation(s)
- Kun Chen
- State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China
| | - Yong Zhang
- IBM Almaden Research Center, San Jose, CA 95120 USA (phone: 408-927-1817, fax: 408-927-3215
| | - Kilian Pohl
- University of Pennsylvania, Philadelphia, PA USA
| | | | - Zhihuan Song
- State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, P.R. China
| | - Stephen TC Wong
- Research Institute, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030 USA
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Hoyos MH, Serfaty JM, Maghiar A, Mansard C, Orkisz M, Magnin IE, Douek PC. Evaluation of semi-automatic arterial stenosis quantification. Int J Comput Assist Radiol Surg 2006. [DOI: 10.1007/s11548-006-0049-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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