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Applying a deep neural network based approach to automating the Micronucleus (MN) assay / QIELLOR HAXHIRAJ

Swansea University Author: QIELLOR HAXHIRAJ

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Abstract

The Micronucleus (MN) Assay is a test mandated for use in genetic toxicology testing by regulatory bodies such as the Food and Drug administration (FDA). An increased quantity of MN is an indication of chromosomal damage which can be characterised into chromosomal breakage (caused by a clastogen) an...

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Published: Swansea 2021
Institution: Swansea University
Degree level: Master of Research
Degree name: MRes
Supervisor: Johnson, George
URI: https://cronfa.swan.ac.uk/Record/cronfa57893
first_indexed 2021-09-14T15:17:51Z
last_indexed 2021-09-15T03:22:05Z
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recordtype RisThesis
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spelling 2021-09-14T16:41:38.1956756 v2 57893 2021-09-14 Applying a deep neural network based approach to automating the Micronucleus (MN) assay d33170d5862a46d7d052387792f64232 QIELLOR HAXHIRAJ QIELLOR HAXHIRAJ true false 2021-09-14 The Micronucleus (MN) Assay is a test mandated for use in genetic toxicology testing by regulatory bodies such as the Food and Drug administration (FDA). An increased quantity of MN is an indication of chromosomal damage which can be characterised into chromosomal breakage (caused by a clastogen) and chromosomal loss (caused by an aneugen). By comparing a dose response, estimates can be made into the potency of the chemical. Historically the cell scoring procedure takes place through the ‘gold standard’ of manual scoring by light microscopy following staining. However, despite being classed the gold standard, this method is laborious and subjective, with archiving of results not a possibility. This leads to the need to develop a new technique to streamline the process, whilst still maintaining accuracy. The result is the creation of a ground truth based deep learning algorithm. By using imaging flow cytometry to carry out the MN assay, a ground truth was created, consisting of different cellular types, including MN. By scoring these images manually by eye, a ground truth of images to teach the deep-learning algorithm is created. By applying a deep neural network, the algorithm uses multiple layers to differentiate information, mimicking the way neurons work in the brain. This approach allows for differentiation between different cellular types based on the ground truth images scored. By assessing more images, the accuracy is further increased. This is advantageous as a MN count is generated directly after processing the imaging flow cytometry file. This streamlines the process completely whilst maintaining accuracy. Also, by using three different laboratory datasets in the production of the ground truth, application was shown to be accurate for cross-laboratory use, a novelty in this research setting. This allows for the existing ground truth to be used for future MN scoring, allowing for the MN assay to be fully automated. E-Thesis Swansea Genetic Toxicology, DNA damage, Micronucleus, Deep learning, Automation 14 9 2021 2021-09-14 COLLEGE NANME COLLEGE CODE Swansea University Johnson, George Master of Research MRes 2021-09-14T16:41:38.1956756 2021-09-14T16:15:16.9948409 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine QIELLOR HAXHIRAJ 1 57893__20849__4f2d402ea48f4ccbb807af572bdb8904.pdf Haxhiraj_Qiellor_MRes_Thesis_Final_Cronfa.pdf 2021-09-14T16:28:39.2524494 Output 5979822 application/pdf E-Thesis – open access true Copyright: The author, Qiellor Haxhiraj, 2021. true eng
title Applying a deep neural network based approach to automating the Micronucleus (MN) assay
spellingShingle Applying a deep neural network based approach to automating the Micronucleus (MN) assay
QIELLOR HAXHIRAJ
title_short Applying a deep neural network based approach to automating the Micronucleus (MN) assay
title_full Applying a deep neural network based approach to automating the Micronucleus (MN) assay
title_fullStr Applying a deep neural network based approach to automating the Micronucleus (MN) assay
title_full_unstemmed Applying a deep neural network based approach to automating the Micronucleus (MN) assay
title_sort Applying a deep neural network based approach to automating the Micronucleus (MN) assay
author_id_str_mv d33170d5862a46d7d052387792f64232
author_id_fullname_str_mv d33170d5862a46d7d052387792f64232_***_QIELLOR HAXHIRAJ
author QIELLOR HAXHIRAJ
author2 QIELLOR HAXHIRAJ
format E-Thesis
publishDate 2021
institution Swansea University
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description The Micronucleus (MN) Assay is a test mandated for use in genetic toxicology testing by regulatory bodies such as the Food and Drug administration (FDA). An increased quantity of MN is an indication of chromosomal damage which can be characterised into chromosomal breakage (caused by a clastogen) and chromosomal loss (caused by an aneugen). By comparing a dose response, estimates can be made into the potency of the chemical. Historically the cell scoring procedure takes place through the ‘gold standard’ of manual scoring by light microscopy following staining. However, despite being classed the gold standard, this method is laborious and subjective, with archiving of results not a possibility. This leads to the need to develop a new technique to streamline the process, whilst still maintaining accuracy. The result is the creation of a ground truth based deep learning algorithm. By using imaging flow cytometry to carry out the MN assay, a ground truth was created, consisting of different cellular types, including MN. By scoring these images manually by eye, a ground truth of images to teach the deep-learning algorithm is created. By applying a deep neural network, the algorithm uses multiple layers to differentiate information, mimicking the way neurons work in the brain. This approach allows for differentiation between different cellular types based on the ground truth images scored. By assessing more images, the accuracy is further increased. This is advantageous as a MN count is generated directly after processing the imaging flow cytometry file. This streamlines the process completely whilst maintaining accuracy. Also, by using three different laboratory datasets in the production of the ground truth, application was shown to be accurate for cross-laboratory use, a novelty in this research setting. This allows for the existing ground truth to be used for future MN scoring, allowing for the MN assay to be fully automated.
published_date 2021-09-14T08:04:47Z
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score 11.047674

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