Detection of replication forks in em images using faster R-Cnn

Wei Zhao, Eleni Maria Manolika, Arnab Ray Chaudhuri, Ihor Smal

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Currently, one of the most effective techniques to study the mechanisms of DNA replication is using the electron microscopy. Typical imaging experiments result in terabytes of image data, where finding and classifying the replication forks is done manually by experts and is extremely time consuming.Here, we present a fully automated deep learning based approach for detection and classification of DNA replication forks. It is based on the Faster R-CNN architecture, following with additional classification layers. The experimental results using real data indicate that the proposed method achieves detection accuracy which approaches the performance of expert annotators, even with limited amounts of training data.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages1786-1789
Number of pages4
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 13 Apr 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

SeriesProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN1945-7928

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Bibliographical note

Publisher Copyright: © 2021 IEEE.

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