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@inproceedings{dhinsey:ipac2021-mopab411,
author = {S.J.K. Dhinsey and T. Greenshaw and J.L. Parsons and C.P. Welsch},
title = {{Quantifying DNA Damage in Comet Assay Images Using Neural Networks}},
booktitle = {Proc. IPAC'21},
pages = {1233--1236},
eid = {MOPAB411},
language = {english},
keywords = {network, proton, software, experiment, radiation},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB411},
url = {https://jacow.org/ipac2021/papers/mopab411.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB411},
abstract = {{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.}},
}