TY - GEN
T1 - Detection of replication forks in em images using faster R-Cnn
AU - Zhao, Wei
AU - Manolika, Eleni Maria
AU - Chaudhuri, Arnab Ray
AU - Smal, Ihor
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85107179001&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434123
DO - 10.1109/ISBI48211.2021.9434123
M3 - Conference proceeding
AN - SCOPUS:85107179001
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1786
EP - 1789
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -