Scotland's Rural College (SRUC), Animal and Veterinary Sciences, Peter Wilson Building, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK.
Sensors (Basel). 2021 Oct 31;21(21):7268. doi: 10.3390/s21217268.
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.
从医学图像中快速准确地检测表型是任何明智和及时响应（如癌症的早期检测或动物繁殖中理想表型的检测）所需要的最重要的品质之一。为了提高这两个质量，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学领域，以提高癌症和 COVID-19 等疾病的检测准确性和速度。在这项研究中，我们应用了深度神经网络，以生成对抗网络（GAN）的形式，对绵羊 CT 扫描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) 管道确定关键表型，如羊腿几何形状和组织分布。使用经过训练的 GAN 进行图像处理的结果在用于未见测试图像时非常相似（相似度指数为 98%）。与手动处理相比，GAN-CV 组合管道能够以每张医学图像 0.11 秒的速度处理和确定表型。我们希望该管道代表了羊遗传育种计划中自动提取表型的第一步。
