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Pan M, Wang Y, Gong P, Wang Q, Cense B. Feasibility of deep learning-based polarization-sensitive optical coherence tomography angiography for imaging cutaneous microvasculature. BIOMEDICAL OPTICS EXPRESS 2023; 14:3856-3870. [PMID: 37799704 PMCID: PMC10549757 DOI: 10.1364/boe.488822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/09/2023] [Accepted: 06/05/2023] [Indexed: 10/07/2023]
Abstract
Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization states of the backscattered light from tissue that can improve angiography based on conventional optical coherence tomography (OCT). We present a feasibility study on PS-OCT integrated with deep learning for PS-OCT angiography (PS-OCTA) imaging of human cutaneous microvasculature. Two neural networks were assessed for PS-OCTA, including the residual dense network (RDN), which previously showed superior performance for angiography with conventional OCT and the upgraded grouped RDN (GRDN). We also investigated different protocols to process the multiple signal channels provided by the Jones matrices from the PS-OCT system to achieve optimal PS-OCTA performance. The training and testing of the deep learning-based PS-OCTA were performed using PS-OCT scans collected from 18 skin locations comprising 16,600 B-scan pairs. The results demonstrated a moderately improved performance of GRDN over RDN, and of the use of the combined signal from the Jones matrix elements over the separate use of the elements, as well as a similar image quality to that provided by speckle decorrelation angiography. GRDN-based PS-OCTA also showed ∼2-3 times faster processing and improved mitigation of tissue motion as compared to speckle decorrelation angiography, and enabled fully automatic processing. Deep learning-based PS-OCTA can be used for imaging cutaneous microvasculature, which may enable easy adoption of PS-OCTA for preclinical and clinical applications.
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Affiliation(s)
- Moning Pan
- Key Laboratory for Biomedical Engineering of Ministry of Education, Embedded System Engineering Research Center of Ministry of Education and Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Zhejiang University, Hangzhou, 310027, China
| | - Yuxing Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Embedded System Engineering Research Center of Ministry of Education and Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Zhejiang University, Hangzhou, 310027, China
| | - Peijun Gong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Embedded System Engineering Research Center of Ministry of Education and Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Zhejiang University, Hangzhou, 310027, China
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands and Centre for Medical Research,
The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Qiang Wang
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Barry Cense
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
- Department of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
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3
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Zeng C, Chen Z, Yang H, Fan Y, Fei L, Chen X, Zhang M. Advanced high resolution three-dimensional imaging to visualize the cerebral neurovascular network in stroke. Int J Biol Sci 2022; 18:552-571. [PMID: 35002509 PMCID: PMC8741851 DOI: 10.7150/ijbs.64373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/28/2021] [Indexed: 11/05/2022] Open
Abstract
As an important method to accurately and timely diagnose stroke and study physiological characteristics and pathological mechanism in it, imaging technology has gone through more than a century of iteration. The interaction of cells densely packed in the brain is three-dimensional (3D), but the flat images brought by traditional visualization methods show only a few cells and ignore connections outside the slices. The increased resolution allows for a more microscopic and underlying view. Today's intuitive 3D imagings of micron or even nanometer scale are showing its essentiality in stroke. In recent years, 3D imaging technology has gained rapid development. With the overhaul of imaging mediums and the innovation of imaging mode, the resolution has been significantly improved, endowing researchers with the capability of holistic observation of a large volume, real-time monitoring of tiny voxels, and quantitative measurement of spatial parameters. In this review, we will summarize the current methods of high-resolution 3D imaging applied in stroke.
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Affiliation(s)
- Chudai Zeng
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Zhuohui Chen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Haojun Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Yishu Fan
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Lujing Fei
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Xinghang Chen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Mengqi Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. NEUROPHOTONICS 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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5
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Wu X, Tao Y, He G, Liu D, Fan M, Yang S, Gong H, Xiao R, Chen S, Huang J. Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain. Front Neurosci 2021; 15:610122. [PMID: 33912000 PMCID: PMC8071950 DOI: 10.3389/fnins.2021.610122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 03/22/2021] [Indexed: 11/17/2022] Open
Abstract
Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to achieve efficient and accurate three-dimensional reconstruction of the vasculature and neural networks in the brain. The success of these tasks heavily depends on the amount, and especially the quality, of the human-annotated labels fed into DCNNs. However, it is often difficult to acquire the gold standard of human-annotated labels for dense nerve tissues; human annotations inevitably contain discrepancies or even errors, which substantially impact the performance of DCNNs. Thus, a novel boosting framework consisting of a DCNN for multilabel semantic segmentation with a customized Dice-logarithmic loss function, a fusion module combining the annotated labels and the corresponding predictions from the DCNN, and a boosting algorithm to sequentially update the sample weights during network training iterations was proposed to systematically improve the quality of the annotated labels; this framework eventually resulted in improved segmentation task performance. The microoptical sectioning tomography (MOST) dataset was then employed to assess the effectiveness of the proposed framework. The result indicated that the framework, even trained with a dataset including some poor-quality human-annotated labels, achieved state-of-the-art performance in the segmentation of somata and vessels in the mouse brain. Thus, the proposed technique of artificial intelligence could advance neuroscience research.
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Affiliation(s)
- Xinglong Wu
- School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, China
| | - Yuhang Tao
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Guangzhi He
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Dun Liu
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Meiling Fan
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Shuo Yang
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
| | - Rong Xiao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
| | - Jin Huang
- Engineering Research Center of Hubei Province for Clothing Information, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China
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7
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Todorov MI, Paetzold JC, Schoppe O, Tetteh G, Shit S, Efremov V, Todorov-Völgyi K, Düring M, Dichgans M, Piraud M, Menze B, Ertürk A. Machine learning analysis of whole mouse brain vasculature. Nat Methods 2020; 17:442-449. [PMID: 32161395 PMCID: PMC7591801 DOI: 10.1038/s41592-020-0792-1] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 02/14/2020] [Indexed: 11/09/2022]
Abstract
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.
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Affiliation(s)
- Mihail Ivilinov Todorov
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Graduate School of Neuroscience (GSN), Munich, Germany
| | - Johannes Christian Paetzold
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Munich School of Bioengineering, Technical University of Munich (TUM), Munich, Germany
| | - Oliver Schoppe
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
| | - Giles Tetteh
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
| | - Suprosanna Shit
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany
- Munich School of Bioengineering, Technical University of Munich (TUM), Munich, Germany
| | - Velizar Efremov
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
- Institute of Pharmacology and Toxicology, University of Zurich (UZH), Zurich, Switzerland
| | - Katalin Todorov-Völgyi
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Marco Düring
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Marie Piraud
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany
| | - Bjoern Menze
- Department of Computer Science, Technical University of Munich (TUM), Munich, Germany.
- Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany.
- Munich School of Bioengineering, Technical University of Munich (TUM), Munich, Germany.
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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