TY - GEN
T1 - Convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay
AU - Ruz-Suarez, Daniel
AU - Martin-Gonzalez, Anabel
AU - Brito-Loeza, Carlos
AU - Pacheco-Pantoja, Elda Leonor
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - The single cell gel electrophoresis assay, which is also referred to as the comet assay, is a quantitative method by which visual evidence of DNA damage in individual cells may be measured. Since this assay is sensitive and simple to perform, it is widely used in several areas including human biomonitoring, genotoxicology, and ecological monitoring. In the last decades, various computer systems have implemented segmentation algorithms based on traditional threshold techniques rather than efficient deep learning methods to automatically identify cells in comet assay output images. This paper presents a fully convolutional neural network based system, named U-NetComet, to automate comets segmentation, minimizing user interaction and providing reproducible measurements. A comparison of our method with a commercial system has been performed, and results showed that our system is more efficient and reliable.
AB - The single cell gel electrophoresis assay, which is also referred to as the comet assay, is a quantitative method by which visual evidence of DNA damage in individual cells may be measured. Since this assay is sensitive and simple to perform, it is widely used in several areas including human biomonitoring, genotoxicology, and ecological monitoring. In the last decades, various computer systems have implemented segmentation algorithms based on traditional threshold techniques rather than efficient deep learning methods to automatically identify cells in comet assay output images. This paper presents a fully convolutional neural network based system, named U-NetComet, to automate comets segmentation, minimizing user interaction and providing reproducible measurements. A comparison of our method with a commercial system has been performed, and results showed that our system is more efficient and reliable.
KW - Comet assay
KW - Convolutional neural network
KW - Deep learning
KW - Segmentation
KW - Single cell gel
UR - http://www.scopus.com/inward/record.url?scp=85127923456&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-98457-1_5
DO - 10.1007/978-3-030-98457-1_5
M3 - Contribución a la conferencia
AN - SCOPUS:85127923456
SN - 9783030984564
T3 - Communications in Computer and Information Science
SP - 57
EP - 68
BT - Intelligent Computing Systems - 4th International Symposium, ISICS 2022, Proceedings
A2 - Brito-Loeza, Carlos
A2 - Martin-Gonzalez, Anabel
A2 - Castañeda-Zeman, Victor
A2 - Safi, Asad
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Symposium on Intelligent Computing Systems, ISICS 2022
Y2 - 23 March 2022 through 25 March 2022
ER -