<  Back to the Polytechnique Montréal portal

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

Waleed Tahir, Sreekanth Kura, Jiabei Zhu, Xiaojun Cheng, Rafat Damseh, Fetsum Tadesse, Alex Seibel, Blaire S. Lee, Frédéric Lesage, Sava Sakadzic, David A. Boas and Lei Tian

Article (2021)

Open Acess document in PolyPublie and at official publisher
Open Access to the full text of this document
Published Version
Terms of Use: Creative Commons Attribution
Download (4MB)
Show abstract
Hide 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.

Additional Information: Le DOI présent sur le document (https://doi.org/10.34133/2021/8620932) ne fonctionne pas.
Subjects: 1900 Biomedical engineering > 1900 Biomedical engineering
Department: Institut de génie biomédical
Funders: NIH
Grant number: R01EB021018-04S2
PolyPublie URL: https://publications.polymtl.ca/9462/
Journal Title: BME Frontiers (vol. 2021)
Publisher: Science Partner Journals
DOI: 10.34133/2020/8620932
Official URL: https://doi.org/10.34133/2020/8620932
Date Deposited: 15 Sep 2023 15:24
Last Modified: 10 Apr 2024 03:14
Cite in APA 7: Tahir, W., Kura, S., Zhu, J., Cheng, X., Damseh, R., Tadesse, F., Seibel, A., Lee, B. S., Lesage, F., Sakadzic, S., Boas, D. A., & Tian, L. (2021). Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning. BME Frontiers, 2021, 8620932 (12 pages). https://doi.org/10.34133/2020/8620932


Total downloads

Downloads per month in the last year

Origin of downloads


Repository Staff Only

View Item View Item