Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline

The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To improve both these qualities, the world is leveraging...

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Autores principales:	James Francis Robson, Scott John Denholm, Mike Coffey
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	generative adversarial network machine learning automated medical image processing deep neural network animal science CT scans Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a9ab647599b74df5b2b93ba334020e43
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spelling	oai:doaj.org-article:a9ab647599b74df5b2b93ba334020e432021-11-11T19:13:45ZAutomated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline10.3390/s212172681424-8220https://doaj.org/article/a9ab647599b74df5b2b93ba334020e432021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7268https://doaj.org/toc/1424-8220The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To improve both these qualities, the world is leveraging artificial intelligence and machine learning against this challenge. Most recently, deep learning has successfully been applied to the medical field to improve detection accuracies and speed for conditions including cancer and COVID-19. In this study, we applied deep neural networks, in the form of a generative adversarial network (GAN), to perform image-to-image processing steps needed for ovine phenotype analysis from CT scans of sheep. Key phenotypes such as gigot geometry and tissue distribution were determined using a computer vision (CV) pipeline. The results of the image processing using a trained GAN are strikingly similar (a similarity index of 98%) when used on unseen test images. The combined GAN-CV pipeline was able to process and determine the phenotypes at a speed of 0.11 s per medical image compared to approximately 30 min for manual processing. We hope this pipeline represents the first step towards automated phenotype extraction for ovine genetic breeding programmes.James Francis RobsonScott John DenholmMike CoffeyMDPI AGarticlegenerative adversarial networkmachine learningautomated medical image processingdeep neural networkanimal scienceCT scansChemical technologyTP1-1185ENSensors, Vol 21, Iss 7268, p 7268 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	generative adversarial network machine learning automated medical image processing deep neural network animal science CT scans Chemical technology TP1-1185
spellingShingle	generative adversarial network machine learning automated medical image processing deep neural network animal science CT scans Chemical technology TP1-1185 James Francis Robson Scott John Denholm Mike Coffey Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
description	The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To improve both these qualities, the world is leveraging artificial intelligence and machine learning against this challenge. Most recently, deep learning has successfully been applied to the medical field to improve detection accuracies and speed for conditions including cancer and COVID-19. In this study, we applied deep neural networks, in the form of a generative adversarial network (GAN), to perform image-to-image processing steps needed for ovine phenotype analysis from CT scans of sheep. Key phenotypes such as gigot geometry and tissue distribution were determined using a computer vision (CV) pipeline. The results of the image processing using a trained GAN are strikingly similar (a similarity index of 98%) when used on unseen test images. The combined GAN-CV pipeline was able to process and determine the phenotypes at a speed of 0.11 s per medical image compared to approximately 30 min for manual processing. We hope this pipeline represents the first step towards automated phenotype extraction for ovine genetic breeding programmes.
format	article
author	James Francis Robson Scott John Denholm Mike Coffey
author_facet	James Francis Robson Scott John Denholm Mike Coffey
author_sort	James Francis Robson
title	Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
title_short	Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
title_full	Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
title_fullStr	Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
title_full_unstemmed	Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline
title_sort	automated processing and phenotype extraction of ovine medical images using a combined generative adversarial network and computer vision pipeline
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a9ab647599b74df5b2b93ba334020e43
work_keys_str_mv	AT jamesfrancisrobson automatedprocessingandphenotypeextractionofovinemedicalimagesusingacombinedgenerativeadversarialnetworkandcomputervisionpipeline AT scottjohndenholm automatedprocessingandphenotypeextractionofovinemedicalimagesusingacombinedgenerativeadversarialnetworkandcomputervisionpipeline AT mikecoffey automatedprocessingandphenotypeextractionofovinemedicalimagesusingacombinedgenerativeadversarialnetworkandcomputervisionpipeline
_version_	1718431603982598144

Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline

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