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Yu T, Yang Q, Peng B, Gu Z, Zhu D. Vascularized organoid-on-a-chip: design, imaging, and analysis. Angiogenesis 2024; 27:147-172. [PMID: 38409567 DOI: 10.1007/s10456-024-09905-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/11/2024] [Indexed: 02/28/2024]
Abstract
Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.
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Affiliation(s)
- Tingting Yu
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Qihang Yang
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Bo Peng
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Dan Zhu
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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2
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Kaeppler JR, Chen J, Buono M, Vermeer J, Kannan P, Cheng W, Voukantsis D, Thompson JM, Hill MA, Allen D, Gomes A, Kersemans V, Kinchesh P, Smart S, Buffa F, Nerlov C, Muschel RJ, Markelc B. Endothelial cell death after ionizing radiation does not impair vascular structure in mouse tumor models. EMBO Rep 2022; 23:e53221. [PMID: 35848459 PMCID: PMC9442312 DOI: 10.15252/embr.202153221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/15/2022] [Accepted: 06/27/2022] [Indexed: 12/24/2022] Open
Abstract
The effect of radiation therapy on tumor vasculature has long been a subject of debate. Increased oxygenation and perfusion have been documented during radiation therapy. Conversely, apoptosis of endothelial cells in irradiated tumors has been proposed as a major contributor to tumor control. To examine these contradictions, we use multiphoton microscopy in two murine tumor models: MC38, a highly vascularized, and B16F10, a moderately vascularized model, grown in transgenic mice with tdTomato-labeled endothelium before and after a single (15 Gy) or fractionated (5 × 3 Gy) dose of radiation. Unexpectedly, even these high doses lead to little structural change of the perfused vasculature. Conversely, non-perfused vessels and blind ends are substantially impaired after radiation accompanied by apoptosis and reduced proliferation of their endothelium. RNAseq analysis of tumor endothelial cells confirms the modification of gene expression in apoptotic and cell cycle regulation pathways after irradiation. Therefore, we conclude that apoptosis of tumor endothelial cells after radiation does not impair vascular structure.
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Affiliation(s)
- Jakob R Kaeppler
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Jianzhou Chen
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Mario Buono
- MRC Molecular Hematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Jenny Vermeer
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Pavitra Kannan
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Wei‐Chen Cheng
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Dimitrios Voukantsis
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - James M Thompson
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Mark A Hill
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Danny Allen
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Ana Gomes
- In Vivo ImagingThe Francis Crick InstituteLondonUK
| | - Veerle Kersemans
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Paul Kinchesh
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Sean Smart
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Francesca Buffa
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Claus Nerlov
- MRC Molecular Hematology Unit, MRC Weatherall Institute of Molecular Medicine, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Ruth J Muschel
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
| | - Bostjan Markelc
- Cancer Research UK and MRC Oxford Institute for Radiation Oncology, Department of OncologyUniversity of OxfordOxfordUK
- Present address:
Department of Experimental OncologyInstitute of Oncology LjubljanaLjubljanaSlovenia
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3
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Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8630449. [PMID: 36035280 PMCID: PMC9410864 DOI: 10.1155/2022/8630449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
To address the problem of low precision in feature segmentation of biological images with large noise, a microfeature segmentation algorithm for biological images using improved density peak clustering was proposed. First, the center pixel and edge information of a biological image were obtained to remove some redundant information. The three-dimensional space of the image is constructed, and the coordinate system is used to describe every superpixel of the biological image. Second, the image symmetry and reversibility are used to obtain the stopping position of pixels, other adjacent points are used to obtain the current color and shape information, and more vectors are used to express the density to complete the image pretreatment. Finally, the improved density peak clustering method is used to cluster the image, and the pixels completed by clustering and the remaining pixels are evenly distributed into the space to segment the image so as to complete the microfeature segmentation of the biological image based on the improved density peak clustering method. The results show that the proposed algorithm improves the segmentation efficiency, segmentation integrity rate, and segmentation accuracy. The time consumed by the proposed biological image microfeature segmentation algorithm is always less than 2 minutes, and the segmentation integrity rate can reach more than 90%. Furthermore, the proposed algorithm can reduce the missing condition and the noise of the segmented image and improve the image feature segmentation effect.
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4
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Stolz BJ, Kaeppler J, Markelc B, Braun F, Lipsmeier F, Muschel RJ, Byrne HM, Harrington HA. Multiscale topology characterizes dynamic tumor vascular networks. SCIENCE ADVANCES 2022; 8:eabm2456. [PMID: 35687679 PMCID: PMC9187234 DOI: 10.1126/sciadv.abm2456] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/27/2022] [Indexed: 06/15/2023]
Abstract
Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the "shape" of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.
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Affiliation(s)
| | - Jakob Kaeppler
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Bostjan Markelc
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Franziska Braun
- Data Science, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany
| | - Florian Lipsmeier
- Digital Biomarkers, pRED Informatics, Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Ruth J. Muschel
- Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Helen M. Byrne
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Heather A. Harrington
- Mathematical Institute, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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5
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Prinke P, Haueisen J, Klee S, Rizqie MQ, Supriyanto E, König K, Breunig HG, Piątek Ł. Automatic segmentation of skin cells in multiphoton data using multi-stage merging. Sci Rep 2021; 11:14534. [PMID: 34267247 PMCID: PMC8282875 DOI: 10.1038/s41598-021-93682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/27/2021] [Indexed: 01/10/2023] Open
Abstract
We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy.
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Affiliation(s)
- Philipp Prinke
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.
| | - Jens Haueisen
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany
| | - Sascha Klee
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Division Biostatistics and Data Science, Department of General Health Studies, Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, 3500, Krems, Austria
| | - Muhammad Qurhanul Rizqie
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Informatics Engineering Program, Universitas Sriwijaya, Palembang, South Sumatera, Indonesia
| | - Eko Supriyanto
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,IJN-UTM Cardiovascular Engineering Centre, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Karsten König
- Department of Biophotonics and Laser Technology, Saarland University, Campus A5.1, 66123, Saarbrücken, Germany.,JenLab GmbH, Johann-Hittorf-Straße 8, 12489, Berlin, Germany
| | - Hans Georg Breunig
- Department of Biophotonics and Laser Technology, Saarland University, Campus A5.1, 66123, Saarbrücken, Germany.,JenLab GmbH, Johann-Hittorf-Straße 8, 12489, Berlin, Germany
| | - Łukasz Piątek
- Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany.,Department of Artificial Intelligence, University of Information Technology and Management, H. Sucharskiego 2 Str, 35-225, Rzeszów, Poland
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6
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Abnormal morphology biases hematocrit distribution in tumor vasculature and contributes to heterogeneity in tissue oxygenation. Proc Natl Acad Sci U S A 2020; 117:27811-27819. [PMID: 33109723 PMCID: PMC7668105 DOI: 10.1073/pnas.2007770117] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Oxygen heterogeneity in solid tumors is recognized as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal tumor vascular structure. We investigate the role that anomalies in red blood cell transport plays in establishing oxygen heterogeneity in tumor tissue. We introduce a metric to characterize tumor vasculature (mean vessel length-to-diameter ratio, λ) and demonstrate how it predicts tissue-oxygen heterogeneity. We also report an increase in λ following treatment with the antiangiogenic agent DC101. Together, we propose λ as an effective way of monitoring the action of antiangiogenic agents and a proxy measure of oxygen heterogeneity in tumor tissue. Unraveling the causal relationship between tumor vascular structure and tissue oxygenation will pave the way for new personalized therapeutic approaches. Oxygen heterogeneity in solid tumors is recognized as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal vascular structure of the tumor, but the precise mechanisms linking abnormal structure and compromised oxygen transport are only partially understood. In this paper, we investigate the role that red blood cell (RBC) transport plays in establishing oxygen heterogeneity in tumor tissue. We focus on heterogeneity driven by network effects, which are challenging to observe experimentally due to the reduced fields of view typically considered. Motivated by our findings of abnormal vascular patterns linked to deviations from current RBC transport theory, we calculated average vessel lengths L¯ and diameters d¯ from tumor allografts of three cancer cell lines and observed a substantial reduction in the ratio λ=L¯/d¯ compared to physiological conditions. Mathematical modeling reveals that small values of the ratio λ (i.e., λ<6) can bias hematocrit distribution in tumor vascular networks and drive heterogeneous oxygenation of tumor tissue. Finally, we show an increase in the value of λ in tumor vascular networks following treatment with the antiangiogenic cancer agent DC101. Based on our findings, we propose λ as an effective way of monitoring the efficacy of antiangiogenic agents and as a proxy measure of perfusion and oxygenation in tumor tissue undergoing antiangiogenic treatment.
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7
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Kirst C, Skriabine S, Vieites-Prado A, Topilko T, Bertin P, Gerschenfeld G, Verny F, Topilko P, Michalski N, Tessier-Lavigne M, Renier N. Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature. Cell 2020; 180:780-795.e25. [PMID: 32059781 DOI: 10.1016/j.cell.2020.01.028] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/20/2019] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
The cerebral vasculature is a dense network of arteries, capillaries, and veins. Quantifying variations of the vascular organization across individuals, brain regions, or disease models is challenging. We used immunolabeling and tissue clearing to image the vascular network of adult mouse brains and developed a pipeline to segment terabyte-sized multichannel images from light sheet microscopy, enabling the construction, analysis, and visualization of vascular graphs composed of over 100 million vessel segments. We generated datasets from over 20 mouse brains, with labeled arteries, veins, and capillaries according to their anatomical regions. We characterized the organization of the vascular network across brain regions, highlighting local adaptations and functional correlates. We propose a classification of cortical regions based on the vascular topology. Finally, we analysed brain-wide rearrangements of the vasculature in animal models of congenital deafness and ischemic stroke, revealing that vascular plasticity and remodeling adopt diverging rules in different models.
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Affiliation(s)
- Christoph Kirst
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France; Center for Physics and Biology and Kavli Neural Systems Insittute, The Rockefeller University, 10065 New York, NY, USA; Kavli Institute for Fundamental Neuroscience and Anatomy Department, Sandler Neuroscience Building, Suite 514G, 675 Nelson Rising Lane, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Sophie Skriabine
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Alba Vieites-Prado
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Thomas Topilko
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Paul Bertin
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | | | - Florine Verny
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Piotr Topilko
- Institut Mondor de Recherche Biomédicale, INSERM U955-Team 9, Créteil, France
| | - Nicolas Michalski
- Unité de Génétique et Physiologie de l'Audition, UMRS 1120, Institut Pasteur, INSERM, 75015 Paris, France
| | | | - Nicolas Renier
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France.
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8
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Papiez BW, Markelc B, Brown G, Muschel RJ, Brady SM, Schnabel JA. Image-Based Artefact Removal in Laser Scanning Microscopy. IEEE Trans Biomed Eng 2020; 67:79-87. [PMID: 31034401 DOI: 10.1109/tbme.2019.2908345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recent developments in laser scanning microscopy have greatly extended its applicability in cancer imaging beyond the visualization of complex biology, and opened up the possibility of quantitative analysis of inherently dynamic biological processes. However, the physics of image acquisition intrinsically means that image quality is subject to a tradeoff between a number of imaging parameters, including resolution, signal-to-noise ratio, and acquisition speed. We address the problem of geometric distortion, in particular, jaggedness artefacts that are caused by the variable motion of the microscope laser, by using a combination of image processing techniques. Image restoration methods have already shown great potential for post-acquisition image analysis. The performance of our proposed image restoration technique was first quantitatively evaluated using phantom data with different textures, and then qualitatively assessed using in vivo biological imaging data. In both cases, the presented method, comprising a combination of image registration and filtering, is demonstrated to have substantial improvement over state-of-the-art microscopy acquisition methods.
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9
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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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10
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Peng T, Wang Y, Xu TC, Shi L, Jiang J, Zhu S. Detection of Lung Contour with Closed Principal Curve and Machine Learning. J Digit Imaging 2018; 31:520-533. [PMID: 29450843 DOI: 10.1007/s10278-018-0058-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10-2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.
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Affiliation(s)
- Tao Peng
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Yihuai Wang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Thomas Canhao Xu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Lianmin Shi
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Jianwu Jiang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Shilang Zhu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
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