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Related Experiment Video

Updated: Jun 28, 2026

An In Vitro 3D Model and Computational Pipeline to Quantify the Vasculogenic Potential of iPSC-Derived Endothelial Progenitors
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An In Vitro 3D Model and Computational Pipeline to Quantify the Vasculogenic Potential of iPSC-Derived Endothelial Progenitors

Published on: May 13, 2019

Segmentation of vessels cluttered with cells using a physics based model.

Stephen J Schmugge1, Steve Keller, Nhat Nguyen

  • 1Department of Computer Science, University of North Carolina at Charlotte, USA. sjschmug@uncc.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for segmenting blood vessels in microscopy images, even with cell clutter. The novel approach significantly improves accuracy in analyzing vascular structures.

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Vascular Biology

Background:

  • Accurate segmentation of vessels in biomedical images is crucial for understanding vascular morphology, topology, and flow dynamics.
  • Intravital microscopy provides in vivo imaging of vasculature and cells, but analysis is challenging due to cell-related image clutter.
  • Existing methods struggle with segmenting vessels in the presence of significant cellular interference.

Purpose of the Study:

  • To develop a novel method for robust vessel segmentation in intravital microscopy images cluttered by cells.
  • To improve the accuracy and reliability of vascular analysis in challenging in vivo imaging scenarios.
  • To overcome limitations of current segmentation techniques when dealing with high levels of cell-related image noise.

Main Methods:

  • A novel method employing virtual point pairs ("vessel probes") that react to forces derived from Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF).
  • Integration of cell detection to guide VVF forces towards vessels and VBVF forces towards vessel boundaries, minimizing distraction from cell image features.
  • Simulation of probe movement based on Newtonian Physics, responding to the net forces applied.

Main Results:

  • Demonstrated effectiveness on five real in vivo liver vasculature images with white blood cell clutter.
  • Achieved a 55% lower Root Mean Squared Error (RMSE) in segmentation compared to methods without VVF and VBVF.
  • Successfully segmented vessels despite significant cell-related image artifacts.

Conclusions:

  • The proposed VVF and VBVF force-based method offers a significant advancement in vessel segmentation accuracy for cluttered intravital microscopy images.
  • This technique enhances the analysis of vascular structures in vivo, particularly in challenging biological contexts.
  • The method provides a more reliable tool for researchers studying vascular dynamics and morphology.