scholarly journals Segmentation-less, automated vascular vectorization robustly extracts neurovascular network statistics from in vivo two-photon images

2020 ◽  
Author(s):  
Samuel A. Mihelic ◽  
William A. Sikora ◽  
Ahmed M. Hassan ◽  
Michael R. Williamson ◽  
Theresa A. Jones ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Statistical Power ◽  
Large Scale ◽  
Performance Metrics ◽  
Three Dimensional ◽  
Vascular Anatomy ◽  
Input Image ◽  
Network Graph ◽  
Two Photon

AbstractRecent advances in two-photon microscopy (2PM) have allowed large scale imaging and analysis of cortical blood vessel networks in living mice. However, extracting a network graph and vector representations for vessels remain bottlenecks in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches require a segmented/binary image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator/trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization/vectorization. To address these limitations, we propose a vectorization method to extract vascular objects directly from unsegmented images. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and low-complexity vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on various, simulated 2PM images based on the large, [1.4, 0.9, 0.6] mm input image, and performance metrics show greater robustness to image quality than an intensity-based thresholding approach.

scholarly journals Segmentation-Less, Automated, Vascular Vectorization

2021 ◽  
Vol 17 (10) ◽  
pp. e1009451
Author(s):  
Samuel A. Mihelic ◽  
William A. Sikora ◽  
Ahmed M. Hassan ◽  
Michael R. Williamson ◽  
Theresa A. Jones ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Statistical Power ◽  
Large Scale ◽  
Volume Fraction ◽  
Three Dimensional ◽  
Vascular Anatomy ◽  
Input Image ◽  
Segmented Images ◽  
Vessel Networks

Recent advances in two-photon fluorescence microscopy (2PM) have allowed large scale imaging and analysis of blood vessel networks in living mice. However, extracting network graphs and vector representations for the dense capillary bed remains a bottleneck in many applications. Vascular vectorization is algorithmically difficult because blood vessels have many shapes and sizes, the samples are often unevenly illuminated, and large image volumes are required to achieve good statistical power. State-of-the-art, three-dimensional, vascular vectorization approaches often require a segmented (binary) image, relying on manual or supervised-machine annotation. Therefore, voxel-by-voxel image segmentation is biased by the human annotator or trainer. Furthermore, segmented images oftentimes require remedial morphological filtering before skeletonization or vectorization. To address these limitations, we present a vectorization method to extract vascular objects directly from unsegmented images without the need for machine learning or training. The Segmentation-Less, Automated, Vascular Vectorization (SLAVV) source code in MATLAB is openly available on GitHub. This novel method uses simple models of vascular anatomy, efficient linear filtering, and vector extraction algorithms to remove the image segmentation requirement, replacing it with manual or automated vector classification. Semi-automated SLAVV is demonstrated on three in vivo 2PM image volumes of microvascular networks (capillaries, arterioles and venules) in the mouse cortex. Vectorization performance is proven robust to the choice of plasma- or endothelial-labeled contrast, and processing costs are shown to scale with input image volume. Fully-automated SLAVV performance is evaluated on simulated 2PM images of varying quality all based on the large (1.4×0.9×0.6 mm3 and 1.6×108 voxel) input image. Vascular statistics of interest (e.g. volume fraction, surface area density) calculated from automatically vectorized images show greater robustness to image quality than those calculated from intensity-thresholded images.

scholarly journals High-speed volumetric two-photon fluorescence imaging of neurovascular dynamics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiang Lan Fan ◽  
Jose A. Rivera ◽  
Wei Sun ◽  
John Peterson ◽  
Henry Haeberle ◽  
...  
Keyword(s):  
Blood Flow ◽  
High Speed ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Laser Scanning Microscopy ◽  
Fluorescence Signal ◽  
Imaging Method ◽  
Spatiotemporal Resolution ◽  
Two Photon

AbstractUnderstanding the structure and function of vasculature in the brain requires us to monitor distributed hemodynamics at high spatial and temporal resolution in three-dimensional (3D) volumes in vivo. Currently, a volumetric vasculature imaging method with sub-capillary spatial resolution and blood flow-resolving speed is lacking. Here, using two-photon laser scanning microscopy (TPLSM) with an axially extended Bessel focus, we capture volumetric hemodynamics in the awake mouse brain at a spatiotemporal resolution sufficient for measuring capillary size and blood flow. With Bessel TPLSM, the fluorescence signal of a vessel becomes proportional to its size, which enables convenient intensity-based analysis of vessel dilation and constriction dynamics in large volumes. We observe entrainment of vasodilation and vasoconstriction with pupil diameter and measure 3D blood flow at 99 volumes/second. Demonstrating high-throughput monitoring of hemodynamics in the awake brain, we expect Bessel TPLSM to make broad impacts on neurovasculature research.

scholarly journals Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning

2019 ◽  
Vol 116 (17) ◽  
pp. 8554-8563 ◽  
Author(s):  
Somayyeh Soltanian-Zadeh ◽  
Kaan Sahingur ◽  
Sarah Blau ◽  
Yiyang Gong ◽  
Sina Farsiu
Keyword(s):  
Real Time ◽  
Calcium Imaging ◽  
Large Scale ◽  
Cortical Layer ◽  
Fast Method ◽  
Active Neuron ◽  
Neuronal Coding ◽  
Two Photon ◽  
Neuron Density

Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.

scholarly journals A platform for brain-wide imaging and reconstruction of individual neurons

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Michael N Economo ◽  
Nathan G Clack ◽  
Luke D Lavis ◽  
Charles R Gerfen ◽  
Karel Svoboda ◽  
...  
Keyword(s):  
Long Range ◽  
High Speed ◽  
Large Scale ◽  
Three Dimensional ◽  
Image Data ◽  
Mammalian Brain ◽  
Projection Neurons ◽  
Computational Tools ◽  
Two Photon ◽  
Axonal Arbors

The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain.

scholarly journals Zebrafish Vascular Quantification (ZVQ): a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis

Development ◽  
2022 ◽  
Author(s):  
E. C. Kugler ◽  
J. Frost ◽  
V. Silva ◽  
K. Plant ◽  
K. Chhabria ◽  
...  
Keyword(s):  
Image Analysis ◽  
Rapid Assessment ◽  
Three Dimensional ◽  
Vascular Anatomy ◽  
Light Sheet ◽  
Automated Image Analysis ◽  
Cerebral Vasculature ◽  
Experimental Conditions ◽  
Branching Points

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification “ZVQ”. It provides the first landmark- or object-based vascular inter-sample registration of the zebrafish cerebral vasculature, producing Population Average Maps allowing rapid assessment of intra- and inter-group vascular anatomy. ZVQ also extracts a range of quantitative vascular parameters from a user-specified Region of Interest including volume, surface area, density, branching points, length, radius, and complexity. Application of ZVQ to thirteen experimental conditions, including embryonic development, pharmacological manipulations and morpholino induced gene knockdown, shows ZVQ is robust, allows extraction of biologically relevant information and quantification of vascular alteration, and can provide novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, ZVQ provides the first open-source quantitative approach to assess the 3D cerebrovascular architecture in zebrafish.

Atherosclerosis imaging

ESC CardioMed ◽  
2018 ◽  
pp. 524-528
Author(s):  
Chun Yuan ◽  
Zach Miller ◽  
Jianming Cai
Keyword(s):  
Atherosclerotic Plaque ◽  
Large Scale ◽  
Three Dimensional ◽  
Plaque Imaging ◽  
Fibrous Cap ◽  
Intraplaque Haemorrhage ◽  
Clinical Events ◽  
Flexible Imaging ◽  
Atherosclerosis Imaging

Atherosclerosis imaging goes beyond the simple identification of luminal stenosis. Besides stenosis measurement, there are two main motivations for atherosclerosis imaging: one is to identify the so-called vulnerable plaque, defined as atherosclerotic plaque that poses increased risk of rupture and clinical events, such as heart attack or stroke; the other is to identify ‘positively remodelled’ plaques—plaques that grow outward from the lumen but cause minimal or no stenosis. Cardiovascular magnetic resonance (CMR) has histologically validated capabilities to characterize carotid plaque features in vivo, including a lipid-rich necrotic core, fibrous cap, intraplaque haemorrhage (IPH), calcification, and inflammation. A multicontrast two-dimensional imaging approach has been used in many prospective studies relating baseline CMR characteristics of carotid atherosclerosis with plaque progression and clinical events. These studies have demonstrated the importance of detecting IPH, lipid-rich necrotic cores, and fibrous caps. Building on these findings, a number of three-dimensional CMR techniques have been recently developed that allow higher spatial resolution plaque imaging and easier application clinically with short scan times. Three-dimensional plaque imaging offers flexible imaging plane and view angle analysis, large coverage, multivascular beds capability, and is a fast and cost-effective screening for clinical use. Atherosclerosis imaging has also been applied to detect plaques in other vascular beds such as the coronary artery, intracranial artery, and peripheral artery, although each bed comes with unique imaging needs. Large-scale studies are needed to determine the impact of atherosclerotic plaque CMR on patient outcomes.

scholarly journals Chronic imaging of mitochondria in the murine cerebral vasculature using in vivo two-photon microscopy

2020 ◽  
Vol 318 (6) ◽  
pp. H1379-H1386
Author(s):  
Ibolya Rutkai ◽  
Wesley R. Evans ◽  
Nikita Bess ◽  
Tomas Salter-Cid ◽  
Siniša Čikić ◽  
...  
Keyword(s):  
Cerebral Circulation ◽  
Three Dimensional ◽  
Cell Types ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Dimensional Reconstruction ◽  
Different Cell Types

We introduce an innovative in vivo approach to study mitochondria in the cerebral circulation in their physiological environment by demonstrating the feasibility of long-term imaging and three-dimensional reconstruction. We postulate that the appropriate combination of Cre/Lox system and two-photon microscopy will contribute to a better understanding of the role of mitochondria in not only endothelium but also the different cell types of the cerebral circulation.

Column-like Ca2+ clusters in the mouse neonatal neocortex revealed by three-dimensional two-photon Ca2+ imaging in vivo

NeuroImage ◽  
2016 ◽  
Vol 138 ◽  
pp. 64-75 ◽  
Author(s):  
Michael Kummer ◽  
Knut Kirmse ◽  
Chuanqiang Zhang ◽  
Jens Haueisen ◽  
Otto W. Witte ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Two Photon

scholarly journals Visualizing synaptic plasticity in vivo by large-scale imaging of endogenous AMPA receptors

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Austin R Graves ◽  
Richard H Roth ◽  
Han L Tan ◽  
Qianwen Zhu ◽  
Alexei M Bygrave ◽  
...  
Keyword(s):  
Ampa Receptors ◽  
Large Scale ◽  
Optical Probe ◽  
Neuronal Communication ◽  
Two Photon ◽  
Dynamic Expression ◽  
Behavioral Paradigm ◽  
Technological Barriers ◽  
Synapse Detection

Elucidating how synaptic molecules such as AMPA receptors mediate neuronal communication and tracking their dynamic expression during behavior is crucial to understand cognition and disease, but current technological barriers preclude large-scale exploration of molecular dynamics in vivo. We have developed a suite of innovative methodologies that break through these barriers: a new knockin mouse line with fluorescently tagged endogenous AMPA receptors, two-photon imaging of hundreds of thousands of labeled synapses in behaving mice, and computer-vision-based automatic synapse detection. Using these tools, we can longitudinally track how the strength of populations of synapses changes during behavior. We used this approach to generate an unprecedentedly detailed spatiotemporal map of synapses undergoing changes in strength following sensory experience. More generally, these tools can be used as an optical probe capable of measuring functional synapse strength across entire brain areas during any behavioral paradigm, describing complex system-wide changes with molecular precision.

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