Deep Semantic Segmentation of Angiogenesis Images.

Clicks: 156
ID: 276560
2023
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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.
Reference Key
ibragimov2023deepinternational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ibragimov, Alisher;Senotrusova, Sofya;Markova, Kseniia;Karpulevich, Evgeny;Ivanov, Andrei;Tyshchuk, Elizaveta;Grebenkina, Polina;Stepanova, Olga;Sirotskaya, Anastasia;Kovaleva, Anastasiia;Oshkolova, Arina;Zementova, Maria;Konstantinova, Viktoriya;Kogan, Igor;Selkov, Sergey;Sokolov, Dmitry;
Journal International journal of molecular sciences
Year 2023
DOI
1102
URL
Keywords
Deep learning angiogenesis semantic segmentation endothelial cells

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