TY - JOUR

T1 - Deep Semantic Segmentation of Angiogenesis Images

AU - Ibragimov, Alisher

AU - Senotrusova, Sofya

AU - Markova, Kseniia

AU - Karpulevich, Evgeny

AU - Ivanov, Andrei

AU - Tyshchuk , Elizaveta

AU - Grebenkina, Polina

AU - Stepanova , Olga

AU - Sirotskaya , Anastasia

AU - Ковалева, Анастасия Андреевна

AU - Oshkolova, Arina

AU - Zementova, Maria

AU - Konstantinova, Viktoriya

AU - Kogan , Igor

AU - Selkov , Sergey

AU - Sokolov, Dmitry

PY - 2023/1/6

Y1 - 2023/1/6

N2 - 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.

AB - 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.

KW - angiogenesis; endothelial cells; deep learning; semantic segmentation

UR - https://www.mendeley.com/catalogue/e0b037ab-00ee-3902-a853-463e67535d8f/

U2 - 10.3390/ijms24021102

DO - 10.3390/ijms24021102

M3 - Article

VL - 24

JO - International Journal of Molecular Sciences

JF - International Journal of Molecular Sciences

SN - 1422-0067

IS - 2

M1 - 1102

ER -