scholarly journals Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

2020 ◽  
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

AbstractObjective and Impact StatementSegmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis.IntroductionVascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms.MethodsWe employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808×808×702 μm.ResultsTo demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope, and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art.ConclusionOur work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

BME Frontiers ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

BME Frontiers ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Waleed Tahir ◽  
Sreekanth Kura ◽  
Jiabei Zhu ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Imaging Systems ◽  
Total Variation Regularization ◽  
Computationally Efficient ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature ◽  
Vascular Segmentation ◽  
Anatomical Modeling

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

scholarly journals FARCI: Fast and Robust Connectome Inference

2021 ◽  
Vol 11 (12) ◽  
pp. 1556
Author(s):  
Saber Meamardoost ◽  
Mahasweta Bhattacharya ◽  
Eun Jung Hwang ◽  
Takaki Komiyama ◽  
Claudia Mewes ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Computationally Efficient ◽  
Noise Levels ◽  
Two Photon ◽  
Computational Speed ◽  
Partial Correlations ◽  
Analysis Package ◽  
Matlab Package ◽  
Neural Anatomy

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

A generalizable deep-learning approach to anatomical modeling of brain vasculature (Conference Presentation)

2020 ◽  
Author(s):  
Waleed Tahir ◽  
Jiabei Zhu ◽  
Sreekanth Kura ◽  
Xiaojun Cheng ◽  
Rafat Damseh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Brain Vasculature ◽  
Anatomical Modeling

scholarly journals Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tianyu Wang ◽  
Chunyan Wu ◽  
Dimitre G Ouzounov ◽  
Wenchao Gu ◽  
Fei Xia ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Large Scale ◽  
Brain Regions ◽  
Excitation Power ◽  
Signal Attenuation ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Tissue Heating ◽  
Photon Excitation ◽  
Deep Brain

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

scholarly journals FARCI: Fast and Robust Connectome Inference

2020 ◽  
Author(s):  
Saber Meamardoost ◽  
Mahasweta Bhattacharya ◽  
EunJung Hwang ◽  
Takaki Komiyama ◽  
Claudia Mewes ◽  
...  
Keyword(s):  
Computational Neuroscience ◽  
Calcium Imaging ◽  
Large Scale ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Noise Levels ◽  
Two Photon ◽  
Computational Speed ◽  
Partial Correlations ◽  
Matlab Package

AbstractThe inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using gold standard datasets from the Neural Connectomics Challenge (NCC) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison to the best performing algorithm in the NCC, FARCI produces more accurate networks over different network sizes and subsampling, while providing over two orders of magnitude faster computational speed.

scholarly journals Imaging brain vasculature with BOLD microscopy: MR detection limits determined by in vivo two-photon microscopy

2008 ◽  
Vol 59 (4) ◽  
pp. 855-865 ◽  
Author(s):  
Sung-Hong Park ◽  
Kazuto Masamoto ◽  
Kristy Hendrich ◽  
Iwao Kanno ◽  
Seong-Gi Kim
Keyword(s):  
Detection Limits ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Brain Vasculature

Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Shu Wang ◽  
Bingbing Lin ◽  
Guimin Lin ◽  
Ruolan Lin ◽  
Feng Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Label Free ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Label Free Detection

scholarly journals Large-scale imaging of cortical dynamics during sensory perception and behavior

2016 ◽  
Vol 115 (6) ◽  
pp. 2852-2866 ◽  
Author(s):  
Joseph B. Wekselblatt ◽  
Erik D. Flister ◽  
Denise M. Piscopo ◽  
Cristopher M. Niell
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Dorsal Surface ◽  
Global Scale ◽  
Orientation Discrimination ◽  
Cortical Dynamics ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Cortical Regions ◽  
And Behavior

Sensory-driven behaviors engage a cascade of cortical regions to process sensory input and generate motor output. To investigate the temporal dynamics of neural activity at this global scale, we have improved and integrated tools to perform functional imaging across large areas of cortex using a transgenic mouse expressing the genetically encoded calcium sensor GCaMP6s, together with a head-fixed visual discrimination behavior. This technique allows imaging of activity across the dorsal surface of cortex, with spatial resolution adequate to detect differential activity in local regions at least as small as 100 μm. Imaging during an orientation discrimination task reveals a progression of activity in different cortical regions associated with different phases of the task. After cortex-wide patterns of activity are determined, we demonstrate the ability to select a region that displayed conspicuous responses for two-photon microscopy and find that activity in populations of individual neurons in that region correlates with locomotion in trained mice. We expect that this paradigm will be a useful probe of information flow and network processing in brain-wide circuits involved in many sensory and cognitive processes.

Sign in / Sign up

Export Citation Format

Share Document