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McGarry SD, Adjekukor C, Ahuja S, Greysson-Wong J, Vien I, Rinker KD, Childs SJ. Vessel Metrics: A software tool for automated analysis of vascular structure in confocal imaging. Microvasc Res 2024; 151:104610. [PMID: 37739214 DOI: 10.1016/j.mvr.2023.104610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/31/2023] [Accepted: 09/09/2023] [Indexed: 09/24/2023]
Abstract
Images contain a wealth of information that is often under analyzed in biological studies. Developmental models of vascular disease are a powerful way to quantify developmentally regulated vessel phenotypes to identify the roots of the disease process. We present vessel Metrics, a software tool specifically designed to analyze developmental vascular microscopy images that will expedite the analysis of vascular images and provide consistency between research groups. We developed a segmentation algorithm that robustly quantifies different image types, developmental stages, organisms, and disease models at a similar accuracy level to a human observer. We validate the algorithm on confocal, lightsheet, and two photon microscopy data in a zebrafish model expressing fluorescent protein in the endothelial nuclei. The tool accurately segments data taken by multiple scientists on varying microscopes. We validate vascular parameters such as vessel density, network length, and diameter, across developmental stages, genetic mutations, and drug treatments, and show a favorable comparison to other freely available software tools. Additionally, we validate the tool in a mouse model. Vessel Metrics reduces the time to analyze experimental results, improves repeatability within and between institutions, and expands the percentage of a given vascular network analyzable in experiments.
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Affiliation(s)
- Sean D McGarry
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada
| | - Cynthia Adjekukor
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada
| | - Suchit Ahuja
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada
| | - Jasper Greysson-Wong
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada
| | - Idy Vien
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada
| | - Kristina D Rinker
- Centre for Bioengineering Research and Education, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Sarah J Childs
- Alberta Children's Hospital Research Institute, University of Calgary, T2N 4N1, Canada; Libin Institute, University of Calgary, T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, University of Calgary, T2N 4N1, Canada.
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Hao Y, Ji A, Xing R, Zhu W, Jiang B, Jian Y, Chen H. Capillaries segmentation of NIR-II images and its application in ischemic stroke. Comput Biol Med 2022; 147:105742. [PMID: 35759993 DOI: 10.1016/j.compbiomed.2022.105742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/05/2022] [Accepted: 06/11/2022] [Indexed: 11/03/2022]
Abstract
Fluorescence imaging in the second near-infrared window (NIR-II) offers μm resolution blood vessel information noninvasively, which is crucial for the diagnosis and surgery treatment of some blood vessel-related diseases. However, only a few blood vessel segmentation algorithms have been done for the NIR-II images so far. Here, we proposed a vessel segmentation algorithm that used multi-scale enhancement and fractional differential to enhance capillaries, and then segmented vessels based on the blood vessels' tubular characteristics. Experimental results showed that this method could effectively suppress the point and lump tissue noise influence during vascular segmentation. The accuracy of vessel identification by other algorithms dropped below 30%, while our algorithm still achieved an accuracy of around 50% in deep vessel segmentation experiments with the 6.5 mm Intralipid. So it had the advantage of accurately detecting deep and dim blood capillaries. Meanwhile, the vascular density quantization algorithm had been successfully applied to the mice's ischemic stroke evaluations for the first time. In addition, this algorithm can provide the quantified vessel features under physiological or pathological conditions, which could be used to accurately evaluate the stroke drugs' therapeutic effect in the future.
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Affiliation(s)
- Yifan Hao
- Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai, 200083, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Aiyan Ji
- Molecular Imaging Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Rongrong Xing
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Wenqing Zhu
- Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai, 200083, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Baohong Jiang
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Yi Jian
- Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai, 200083, China.
| | - Hao Chen
- Molecular Imaging Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
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Song S, Du C, Chen Y, Ai D, Song H, Huang Y, Wang Y, Yang J. Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence. BMC Med Inform Decis Mak 2019; 19:270. [PMID: 31856807 PMCID: PMC6921392 DOI: 10.1186/s12911-019-0966-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.
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Affiliation(s)
- Shuang Song
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Chenbing Du
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Ying Chen
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Danni Ai
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Hong Song
- AICFVE of Beijing Film Academy, 4 Xitucheng Rd, Haidian, Beijing, 100088, China
| | - Yong Huang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yongtian Wang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.,School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
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Vakharia VN, Sparks R, Vos SB, McEvoy AW, Miserocchi A, Ourselin S, Duncan JS. The Effect of Vascular Segmentation Methods on Stereotactic Trajectory Planning for Drug-Resistant Focal Epilepsy: A Retrospective Cohort Study. World Neurosurg X 2019; 4:100057. [PMID: 31650126 DOI: 10.1016/j.wnsx.2019.100057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/26/2019] [Accepted: 07/27/2019] [Indexed: 11/23/2022] Open
Abstract
Background Stereotactic neurosurgical procedures carry a risk of intracranial hemorrhage, which may result in significant morbidity and mortality. Vascular imaging is crucial for planning stereotactic procedures to prevent conflicts with intracranial vasculature. There is a wide range of vascular imaging methods used for stereoelectroencephalography (SEEG) trajectory planning. Computer-assisted planning (CAP) improves planning time and trajectory metrics. We aimed to quantify the effect of different vascular imaging protocols on CAP trajectories for SEEG. Methods Ten patients who had undergone SEEG (95 electrodes) following preoperative acquisition of gadolinium-enhanced magnetic resonance imaging (MR + Gad), magnetic resonance angiography and magnetic resonance angiography (MRV + MRA), and digital subtraction catheter angiography (DSA) were identified from a prospectively maintained database. SEEG implantations were planned using CAP using DSA segmentations as the gold standard. Strategies were then recreated using MRV + MRA and MR + Gad to define the “apparent” and “true” risk scores associated with each modality. Vessels of varying diameter were then iteratively removed from the DSA segmentation to identify the size at which all 3 vascular modalities returned the same safety metrics. Results CAP performed using DSA vessel segmentations resulted in significantly lower “true” risk scores and greater minimum distances from vasculature compared with the “true” risk associated with MR + Gad and MRV + MRA. MRV + MRA and MR + Gad returned similar risk scores to DSA when vessels <2 mm and <4 mm were not considered, respectively. Conclusions Significant variability in vascular imaging and trajectory planning practices exist for SEEG. CAP performed with MR + Gad or MRV + MRA alone returns “falsely” lower risk scores compared with DSA. It is unclear whether DSA is oversensitive and thus restricting potential trajectories.
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Key Words
- CAP, Computer-assisted planning
- Computer-assisted planning
- DSA, Digital subtraction catheter angiography
- EpiNav
- Epilepsy
- GIF, Geodesic information flows
- GM, Gray matter
- MD, Minimum distance
- MPRAGE, Magnetization prepared-rapid gradient echo
- MRA, Magnetic resonance angiography
- MRV, Magnetic resonance venography
- MR + Gad, Gadolinium-enhanced magnetic resonance imaging
- ROI, Region of interest
- RS, Risk score
- SEEG, Stereoelectroencephalography
- Stereoelectroencephalography
- Vascular segmentation
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Xiao R, Ding H, Zhai F, Zhao T, Zhou W, Wang G. Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features. Comput Methods Programs Biomed 2017; 142:157-166. [PMID: 28325443 DOI: 10.1016/j.cmpb.2017.02.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/24/2017] [Accepted: 02/09/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset. METHODS First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation. RESULTS Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios. CONCLUSIONS The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.
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Affiliation(s)
- Ruoxiu Xiao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Hui Ding
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Fangwen Zhai
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Tong Zhao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Wenjing Zhou
- Tsinghua University Yuquan Hospital, No. 5, Shijingshan Road, Shijingshan District, Beijing, 100049, China
| | - Guangzhi Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
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Massimi L, Fratini M, Bukreeva I, Brun F, Mittone A, Campi G, Spanò R, Mastrogiacomo M, de Rosbo NK, Bravin A, Uccelli A, Cedola A. Characterization of mouse spinal cord vascular network by means of synchrotron radiation X-ray phase contrast tomography. Phys Med 2016; 32:1779-1784. [PMID: 27743707 DOI: 10.1016/j.ejmp.2016.09.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 07/24/2016] [Accepted: 09/22/2016] [Indexed: 11/29/2022] Open
Abstract
High resolution Synchrotron-based X-ray Phase Contrast Tomography (XPCT) allows the simultaneous detection of three dimensional neuronal and vascular networks without using contrast agents or invasive casting preparation. We show and discuss the different features observed in reconstructed XPCT volumes of the ex vivo mouse spinal cord in the lumbo-sacral region, including motor neurons and blood vessels. We report the application of an intensity-based segmentation method to detect and quantitatively characterize the modification in the vascular networks in terms of reduction in experimental visibility. In particular, we apply our approach to the case of the experimental autoimmune encephalomyelitis (EAE), i.e. human multiple sclerosis animal model.
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Affiliation(s)
- Lorenzo Massimi
- Consiglio Nazionale delle Ricerche, Istituto di Nanotecnologia, Rome Unit, I-00195 Rome, Italy.
| | - Michela Fratini
- Consiglio Nazionale delle Ricerche, Istituto di Nanotecnologia, Rome Unit, I-00195 Rome, Italy; Fondazione Santa Lucia IRCCS, 00179 Roma, Italy
| | - Inna Bukreeva
- Consiglio Nazionale delle Ricerche, Istituto di Nanotecnologia, Rome Unit, I-00195 Rome, Italy; P.N. Lebedev Physical Institute, Russian Academy of Sciences, Leninskii pr., 53 Moscow, Russia
| | - Francesco Brun
- Consiglio Nazionale delle Ricerche, Istituto di Nanotecnologia, Rome Unit, I-00195 Rome, Italy
| | - Alberto Mittone
- European Synchrotron Radiation Facility, F-38043 Grenoble, Cedex 9, France
| | - Gaetano Campi
- Institute of Crystallography-CNR, Monterotondo, Rome, Italy
| | - Raffaele Spanò
- Department of Experimental Medicine, University of Genova & AUO San Martino - IST Istituto Nazionale per la Ricerca sul Cancro, Largo R. Benzi 10, 16132 Genova, Italy
| | - Milena Mastrogiacomo
- Department of Experimental Medicine, University of Genova & AUO San Martino - IST Istituto Nazionale per la Ricerca sul Cancro, Largo R. Benzi 10, 16132 Genova, Italy
| | | | - Alberto Bravin
- European Synchrotron Radiation Facility, F-38043 Grenoble, Cedex 9, France
| | - Antonio Uccelli
- University of Genova DINOGMI Largo Daneo, 3 IT-16132 Genova, Italy
| | - Alessia Cedola
- Consiglio Nazionale delle Ricerche, Istituto di Nanotecnologia, Rome Unit, I-00195 Rome, Italy
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Ibrahim G, Rona A, Hainsworth SV. Non-uniform central airways ventilation model based on vascular segmentation. Comput Biol Med 2015; 65:137-45. [PMID: 26318114 DOI: 10.1016/j.compbiomed.2015.08.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 08/04/2015] [Accepted: 08/06/2015] [Indexed: 11/17/2022]
Abstract
Improvements in the understanding of the physiology of the central airways require an appropriate representation of the non-uniform ventilation at its terminal branches. This paper proposes a new technique for estimating the non-uniform ventilation at the terminal branches by modelling the volume change of their distal peripheral airways, based on vascular segmentation. The vascular tree is used for sectioning the dynamic CT-based 3D volume of the lung at 11 time points over the breathing cycle of a research animal. Based on the mechanical coupling between the vascular tree and the remaining lung tissues, the volume change of each individual lung segment over the breathing cycle was used to estimate the non-uniform ventilation of its associated terminal branch. The 3D lung sectioning technique was validated on an airway cast model of the same animal pruned to represent the truncated dynamic CT based airway geometry. The results showed that the 3D lung sectioning technique was able to estimate the volume of the missing peripheral airways within a tolerance of 2%. In addition, the time-varying non-uniform ventilation distribution predicted by the proposed sectioning technique was validated against CT measurements of lobar ventilation and showed good agreement. This significant modelling advance can be used to estimate subject-specific non-uniform boundary conditions to obtain subject-specific numerical models of the central airway flow.
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Affiliation(s)
- G Ibrahim
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - A Rona
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - S V Hainsworth
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
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