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Tahir W, Kura S, Zhu J, Cheng X, Damseh R, Tadesse F, Seibel A, Lee BS, Lesage F, Sakadžic S, Boas DA, Tian L. Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning. BME Frontiers 2021. [DOI: 10.34133/2021/8620932] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
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.
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Affiliation(s)
- Waleed Tahir
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Sreekanth Kura
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jiabei Zhu
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Xiaojun Cheng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Rafat Damseh
- Biomedical Engineering Institute, École Polytechnique de Montréal, Montréal, QC, Canada
| | - Fetsum Tadesse
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Alex Seibel
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Blaire S. Lee
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Institute of Neurological Sciences and Psychiatry, Hacettepe University, Ankara, Turkey
| | - Frédéric Lesage
- Biomedical Engineering Institute, École Polytechnique de Montréal, Montréal, QC, Canada
| | - Sava Sakadžic
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA
| | - David A. Boas
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Lei Tian
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
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Tahir W, Kura S, Zhu J, Cheng X, Damseh R, Tadesse F, Seibel A, Lee BS, Lesage F, Sakadžic S, Boas DA, Tian L. Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning. BME Front 2020; 2020:8620932. [PMID: 37849965 PMCID: PMC10521669 DOI: 10.34133/2020/8620932] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 11/12/2020] [Indexed: 10/19/2023] Open
Abstract
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 μ m . 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.
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Affiliation(s)
- Waleed Tahir
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Sreekanth Kura
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jiabei Zhu
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Xiaojun Cheng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Rafat Damseh
- Biomedical Engineering Institute, École Polytechnique de Montréal, Montréal, QC, Canada
| | - Fetsum Tadesse
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Alex Seibel
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Blaire S. Lee
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Institute of Neurological Sciences and Psychiatry, Hacettepe University, Ankara, Turkey
| | - Frédéric Lesage
- Biomedical Engineering Institute, École Polytechnique de Montréal, Montréal, QC, Canada
| | - Sava Sakadžic
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA
| | - David A. Boas
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Lei Tian
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
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Robinson M, Boas D, Sakadžic S, Franceschini MA, Carp S. Interferometric diffuse correlation spectroscopy improves measurements at long source-detector separation and low photon count rate. J Biomed Opt 2020; 25:JBO-200232R. [PMID: 33000571 PMCID: PMC7525153 DOI: 10.1117/1.jbo.25.9.097004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/11/2020] [Indexed: 05/04/2023]
Abstract
SIGNIFICANCE The use of diffuse correlation spectroscopy (DCS) has shown efficacy in research studies as a technique capable of noninvasively monitoring blood flow in tissue with applications in neuromonitoring, exercise science, and breast cancer management. The ability of DCS to resolve blood flow in these tissues is related to the optical sensitivity and signal-to-noise ratio (SNR) of the measurements, which in some cases, particularly adult cerebral blood flow measurements, is inadequate in a significant portion of the population. Improvements to DCS sensitivity and SNR could allow for greater clinical translation of this technique. AIM Interferometric diffuse correlation spectroscopy (iDCS) was characterized and compared to traditional homodyne DCS to determine possible benefits of utilizing heterodyne detection. APPROACH An iDCS system was constructed by modifying a homodyne DCS system with fused fiber couplers to create a Mach-Zehnder interferometer. Comparisons between homodyne and heterodyne detection were performed using an intralipid phantom characterized at two extended source-detector separations (2.4, 3.6 cm), different photon count rates, and a range of reference arm power levels. Characterization of the iDCS signal mixing was compared to theory. Precision of the estimation of the diffusion coefficient and SNR of the autocorrelation curve were compared between different measurement conditions that mimicked what would be seen in vivo. RESULTS The mixture of signals present in the heterodyne autocorrelation function was found to agree with the derived theory and resulted in accurate measurement of the diffusion coefficient of the phantom. Improvement of the SNR of the autocorrelation curve up to ∼2 × and up to 80% reduction in the variability of the diffusion coefficient fit were observed for all measurement cases as a function of increased reference arm power. CONCLUSIONS iDCS has the potential to improve characterization of blood flow in tissue at extended source-detector separations, enhancing depth sensitivity and SNR.
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Affiliation(s)
- Mitchell Robinson
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, United States
- Harvard-MIT Health Sciences and Technology, United States
- Harvard Medical School, United States
| | - David Boas
- Neurophotonics Ctr., Boston Univ., United States
| | - Sava Sakadžic
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, United States
- Harvard Medical School, United States
| | - Maria Angela Franceschini
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, United States
- Harvard Medical School, United States
| | - Stefan Carp
- Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, United States
- Harvard Medical School, United States
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