ArticleFrontiers in physiology2021
Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Fractal analysis of hepatocellular carcinoma vasculature shows regional differences independent of vascular invasion.Scientific reports · 2026Article
- Isolated human adipose microvessels retain native microvessel structure and recapitulate sprouting angiogenesis.Angiogenesis · 2025Article
- Three-Dimensional Curved Workflow-Based Optical Coherence Tomography Angiography for Enhancing Atopic Dermatitis Theranostics.Research (Washington, D.C.) · 2025Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
- Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning.Annals of biomedical engineering · 2023Article
- Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning.Biomedical optics express · 2023Article
- Article
- Engineering approaches for cardiac organoid formation and their characterization.Translational research : the journal of laboratory and clinical medicine · 2022Review
- Vascularizing the brainiScience · 2022Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.