Standard

Deep Semantic Segmentation of Angiogenesis Images. / Ibragimov, Alisher ; Senotrusova, Sofya ; Markova, Kseniia; Karpulevich, Evgeny ; Ivanov, Andrei ; Tyshchuk , Elizaveta ; Grebenkina, Polina ; Stepanova , Olga ; Sirotskaya , Anastasia ; Ковалева, Анастасия Андреевна; Oshkolova, Arina ; Zementova, Maria ; Konstantinova, Viktoriya ; Kogan , Igor ; Selkov , Sergey ; Sokolov, Dmitry .

в: International Journal of Molecular Sciences, Том 24, № 2, 1102, 06.01.2023.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

Harvard

Ibragimov, A, Senotrusova, S, Markova, K, Karpulevich, E, Ivanov, A, Tyshchuk , E, Grebenkina, P, Stepanova , O, Sirotskaya , A, Ковалева, АА, Oshkolova, A, Zementova, M, Konstantinova, V, Kogan , I, Selkov , S & Sokolov, D 2023, 'Deep Semantic Segmentation of Angiogenesis Images', International Journal of Molecular Sciences, Том. 24, № 2, 1102. https://doi.org/10.3390/ijms24021102

APA

Ibragimov, A., Senotrusova, S., Markova, K., Karpulevich, E., Ivanov, A., Tyshchuk , E., Grebenkina, P., Stepanova , O., Sirotskaya , A., Ковалева, А. А., Oshkolova, A., Zementova, M., Konstantinova, V., Kogan , I., Selkov , S., & Sokolov, D. (2023). Deep Semantic Segmentation of Angiogenesis Images. International Journal of Molecular Sciences, 24(2), [1102]. https://doi.org/10.3390/ijms24021102

Vancouver

Ibragimov A, Senotrusova S, Markova K, Karpulevich E, Ivanov A, Tyshchuk E и пр. Deep Semantic Segmentation of Angiogenesis Images. International Journal of Molecular Sciences. 2023 Янв. 6;24(2). 1102. https://doi.org/10.3390/ijms24021102

Author

Ibragimov, Alisher ; Senotrusova, Sofya ; Markova, Kseniia ; Karpulevich, Evgeny ; Ivanov, Andrei ; Tyshchuk , Elizaveta ; Grebenkina, Polina ; Stepanova , Olga ; Sirotskaya , Anastasia ; Ковалева, Анастасия Андреевна ; Oshkolova, Arina ; Zementova, Maria ; Konstantinova, Viktoriya ; Kogan , Igor ; Selkov , Sergey ; Sokolov, Dmitry . / Deep Semantic Segmentation of Angiogenesis Images. в: International Journal of Molecular Sciences. 2023 ; Том 24, № 2.

BibTeX

@article{6c6a356f14154181a7fb02e8068e3468,
title = "Deep Semantic Segmentation of Angiogenesis Images",
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.",
keywords = "angiogenesis; endothelial cells; deep learning; semantic segmentation",
author = "Alisher Ibragimov and Sofya Senotrusova and Kseniia Markova and Evgeny Karpulevich and Andrei Ivanov and Elizaveta Tyshchuk and Polina Grebenkina and Olga Stepanova and Anastasia Sirotskaya and Ковалева, {Анастасия Андреевна} and Arina Oshkolova and Maria Zementova and Viktoriya Konstantinova and Igor Kogan and Sergey Selkov and Dmitry Sokolov",
year = "2023",
month = jan,
day = "6",
doi = "10.3390/ijms24021102",
language = "English",
volume = "24",
journal = "International Journal of Molecular Sciences",
issn = "1422-0067",
publisher = "MDPI AG",
number = "2",

}

RIS

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 -

ID: 102226714