ArticleInternational journal of molecular sciences2023
Deep Semantic Segmentation of Angiogenesis Images.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Linking preclinical models with functional imaging and artificial intelligence to decode tumor angiogenesis.Pharmacological reports : PR · 2026Review
- Rethinking DeepVariant: Efficient Neural Architectures for Intelligent Variant Calling.International journal of molecular sciences · 2026Article
- Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers.Journal of clinical medicine · 2026Article
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- An accurate semantic segmentation model for bean seedlings and weeds identification based on improved ERFnet.Scientific reports · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder-decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out.
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Registered trials
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