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Related Experiment Video

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Segmentation-Less, Automated, Vascular Vectorization.

Samuel A Mihelic1, William A Sikora1, Ahmed M Hassan1

  • 1Department of Biomedical Engineering, The University of Texas, Austin, Texas, United States of America.

Plos Computational Biology
|October 8, 2021
PubMed
Summary

We developed a new method for vascular vectorization that works directly on unsegmented images, bypassing the need for manual annotation or machine learning. This approach, Segmentation-Less, Automated, Vascular Vectorization (SLAVV), simplifies blood vessel analysis in living mice.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Two-photon fluorescence microscopy (2PM) enables large-scale imaging of blood vessel networks in vivo.
  • Extracting detailed vascular graphs, especially from dense capillary beds, remains a significant challenge.
  • Current 3D vascular vectorization methods often depend on image segmentation, introducing bias and requiring extensive preprocessing.

Purpose of the Study:

  • To develop a novel vascular vectorization method that eliminates the need for image segmentation and machine learning.
  • To provide an open-source, automated tool for efficient and accurate blood vessel network analysis.
  • To improve the robustness and reduce the bias in extracting vascular features from complex biological images.

Main Methods:

  • Developed Segmentation-Less, Automated, Vascular Vectorization (SLAVV) using MATLAB, employing linear filtering and vector extraction on unsegmented 2PM images.
  • Utilized simple models of vascular anatomy to directly extract vascular objects without prior segmentation.
  • Demonstrated semi-automated SLAVV on in vivo 2PM image volumes of mouse cortical microvasculature and evaluated fully-automated SLAVV on simulated images.

Main Results:

  • SLAVV successfully extracts vascular networks directly from unsegmented images, removing the dependency on manual or machine learning-based segmentation.
  • The method is robust to different contrast agents (plasma- or endothelial-labeled) and processing costs scale with image volume.
  • Vascular statistics derived from SLAVV-vectorized images show improved robustness to image quality variations compared to intensity-thresholded methods.

Conclusions:

  • SLAVV offers a significant advancement in vascular vectorization by removing the segmentation bottleneck, enabling more efficient and less biased analysis.
  • The open-source availability of SLAVV facilitates broader application in neuroscience and biomedical research.
  • This method enhances the reliability of quantitative vascular analysis from in vivo microscopy data.