End2end image analysis of single-cell gel  electrophoresis

dc.contributor.author	Hirna, Mariya
dc.date.accessioned	2024-02-15T09:30:58Z
dc.date.available	2024-02-15T09:30:58Z
dc.date.issued	2020
dc.identifier.citation	Hirna, Mariya. End2end image analysis of single-cell gel electrophoresis / Hirna, Mariya; Supervisor: Igor Krashenyi; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2020. – 38 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4516
dc.language.iso	en	uk
dc.title	End2end image analysis of single-cell gel electrophoresis	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Single-cell gel electrophoresis is the standard test used by biomedical researchers to analyze damage to the cell. Currently, this test is only done using standard image processing techniques, that skews the outputs, requires manual work and/or human supervision. Other problems with current solutions include poor usability, lack of flexibility, and high price for commercial applications. In this work, we create a deep learning-based end2end pipeline, that receives images from the test as an input, and produces damage metrics as an output. We have trained UNet with SE-ResNet50 encoder on the custom-created synthetic dataset, which achieves the dice coefficient of 76.8. We hope that the results of this work will become the base of the easy-to-use open-source application available for any researcher.	uk