IPAC2021 - List of Authors (Parsons, J.L.)

Paper	Title	Page
MOPAB411	Quantifying DNA Damage in Comet Assay Images Using Neural Networks	1233
	S.J.K. Dhinsey, T. Greenshaw, C.P. Welsch The University of Liverpool, Liverpool, United Kingdom J.L. Parsons Cancer Research Centre, University of Liverpool, Liverpool, United Kingdom C.P. Welsch Cockcroft Institute, Warrington, Cheshire, United Kingdom
	Funding: This work was supported by the STFC Liverpool Centre for Doctoral Training on Data Intensive Science (LIV. DAT) under grant agreement ST/P006752/1. Proton therapy for cancer treatment is a rapidly growing field and increasing evidence suggests it induces more complex DNA damage than photon therapy. Accurate comparison between the two treatments requires quantification of the DNA damage the cause, which can be assessed using the Comet Assay. The program outlined here is based on neural network architecture and aims to speed up analysis of Comet Assay images and provide accurate, quantifiable assessment of the DNA damage levels apparent in individual cells. The Comet Assay is an established technique in which DNA fragments are spread out under the influence of an electric field, producing a comet-like object. The elongation and intensity of the comet tail (consisting of DNA fragments) indicate the level of damage incurred. Many methods to measure this damage exist, using a variety of algorithms. However, these can be time consuming, so often only a small fraction of the comets available in an image are analysed. The automatic analysis presented in this contribution aims to improve this. To supplement the training and testing of the network, a Monte Carlo model will also be presented to create simulated comet assay images.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB411
About •	paper received ※ 19 May 2021 paper accepted ※ 09 June 2021 issue date ※ 16 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Quantifying DNA Damage in Comet Assay Images Using Neural Networks

1233

S.J.K. Dhinsey, T. Greenshaw, C.P. Welsch
The University of Liverpool, Liverpool, United Kingdom
J.L. Parsons
Cancer Research Centre, University of Liverpool, Liverpool, United Kingdom
C.P. Welsch
Cockcroft Institute, Warrington, Cheshire, United Kingdom

Funding: This work was supported by the STFC Liverpool Centre for Doctoral Training on Data Intensive Science (LIV. DAT) under grant agreement ST/P006752/1.
Proton therapy for cancer treatment is a rapidly growing field and increasing evidence suggests it induces more complex DNA damage than photon therapy. Accurate comparison between the two treatments requires quantification of the DNA damage the cause, which can be assessed using the Comet Assay. The program outlined here is based on neural network architecture and aims to speed up analysis of Comet Assay images and provide accurate, quantifiable assessment of the DNA damage levels apparent in individual cells. The Comet Assay is an established technique in which DNA fragments are spread out under the influence of an electric field, producing a comet-like object. The elongation and intensity of the comet tail (consisting of DNA fragments) indicate the level of damage incurred. Many methods to measure this damage exist, using a variety of algorithms. However, these can be time consuming, so often only a small fraction of the comets available in an image are analysed. The automatic analysis presented in this contribution aims to improve this. To supplement the training and testing of the network, a Monte Carlo model will also be presented to create simulated comet assay images.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB411

About •

paper received ※ 19 May 2021 paper accepted ※ 09 June 2021 issue date ※ 16 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)