ArticleAnnals of biomedical engineering2022
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology.
Article in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prospective Intraindividual Split-face Histological Comparison of Poly-d,l-lactic Acid and Calcium Hydroxylapatite Skin Boosters Using Stem Cell Markers.Plastic and reconstructive surgery. Global open · 2026Article
- CD34 evaluation of microvasculature in lung adenocarcinoma and its microvascular density predicts postoperative tumor recurrence.Pathology oncology research : POR · 2025Article
- An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing.Scientific reports · 2024Article
- VEGF-delivering PEG hydrogels promote vascularization in the porcine subcutaneous space.Journal of biomedical materials research. Part A · 2024Article
- ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.Frontiers in bioengineering and biotechnology · 2024Article
- Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.Biomolecules · 2023Review
- An Overview of Angiogenesis in Bladder Cancer.Current oncology reports · 2023Review
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Authors and funding
9 authors.
Funding
Abstract
Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator's experience. In this study, we present "blood vessel detection-BVD", an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.
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Registered trials
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