Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline.
Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline.
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DOI：
10.3390/s21217268
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发表时间：
2021-10-31
期刊：
影响因子：
--
通讯作者：
Coffey M
中科院分区：
文献类型：
--
作者：
Robson JF;Denholm SJ;Coffey M
从医学图像中检测表型的速度和准确性是任何明智和及时的反应所需的一些最重要的品质，例如癌症的早期检测或动物育种所需表型的检测。为了提高这两个品质，世界正在利用人工智能和机器学习来应对这一挑战。最近，深度学习已成功应用于医疗领域，以提高癌症和 COVID-19 等疾病的检测精度和速度。在这项研究中，我们应用生成对抗网络 (GAN) 形式的深度神经网络来执行从绵羊 CT 扫描中进行绵羊表型分析所需的图像到图像处理步骤。使用计算机视觉（CV）管道确定了关键表型，例如牛腿几何形状和组织分布。当用于未见过的测试图像时，使用经过训练的 GAN 进行图像处理的结果惊人地相似（相似度指数为 98%）。组合的 GAN-CV 管道能够以每张医学图像 0.11 秒的速度处理和确定表型，而手动处理大约需要 30 分钟。我们希望这条管道代表了绵羊遗传育种计划自动化表型提取的第一步。
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.
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影响因子：
16.6
作者：
Li, Xin;Yang, Ji;Li, Meng-Hua
通讯作者：
Li, Meng-Hua
影响因子：
16.6
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Lassau N;Ammari S;Chouzenoux E;Gortais H;Herent P;Devilder M;Soliman S;Meyrignac O;Talabard MP;Lamarque JP;Dubois R;Loiseau N;Trichelair P;Bendjebbar E;Garcia G;Balleyguier C;Merad M;Stoclin A;Jegou S;Griscelli F;Tetelboum N;Li Y;Verma S;Terris M;Dardouri T;Gupta K;Neacsu A;Chemouni F;Sefta M;Jehanno P;Bousaid I;Boursin Y;Planchet E;Azoulay M;Dachary J;Brulport F;Gonzalez A;Dehaene O;Schiratti JB;Schutte K;Pesquet JC;Talbot H;Pronier E;Wainrib G;Clozel T;Barlesi F;Bellin MF;Blum MGB
通讯作者：
Blum MGB
影响因子：
19.7
作者：
Li, Lin;Qin, Lixin;Xia, Jun
通讯作者：
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DOI：
10.1111/1467-9876.00264
发表时间：
2002-01-01
影响因子：
1.6
作者：
Glasbey, CA;Young, MJ
通讯作者：
Young, MJ
DOI：
10.3390/s21113758
发表时间：
2021-05-28
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Benos L;Tagarakis AC;Dolias G;Berruto R;Kateris D;Bochtis D
通讯作者：
Bochtis D