López Vargas Jean Pierre Brik, de Abreu Katariny Lima, Duarte de Paula Davi, Pinheiro Salvadeo Denis Henrique, Arantes de Souza Lilian Francisco, Bôa-Viagem Rabello Carlos
Institute of Geosciences and Exact Sciences, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.
Zootechnics Department, Federal Rural University of Pernambuco (UFRPE), Recife 52171-900, PE, Brazil.
Foods. 2024 Dec 14;13(24):4039. doi: 10.3390/foods13244039.
The egg has natural barriers that prevent microbiological contamination and promote food safety. The use of non-destructive methods to obtain morphometric measurements of chicken eggs has the potential to replace traditional invasive techniques, offering greater efficiency and accuracy. This paper aims to demonstrate that estimates derived from non-invasive approaches, such as 3D computed tomography (CT) image analysis, can be comparable to conventional destructive methods. To achieve this goal, two widely recognized deep learning architectures, U-Net 3D and Fully Convolutional Networks (FCN) 3D, were modeled to segment and analyze 3D CT images of chicken eggs. A dataset of real CT images was created and labeled, allowing the extraction of important morphometric measurements, including height, width, shell thickness, and volume. The models achieved an accuracy of up to 98.69%, demonstrating their effectiveness compared to results from manual measurements. These findings highlight the potential of CT image analysis, combined with deep learning, as a non-invasive alternative in industrial and research settings. This approach not only minimizes the need for invasive procedures but also offers a scalable and reliable method for egg quality assessment.
鸡蛋具有天然屏障，可防止微生物污染并促进食品安全。使用非破坏性方法获取鸡蛋的形态测量数据有可能取代传统的侵入性技术，从而提高效率和准确性。本文旨在证明，通过非侵入性方法（如三维计算机断层扫描（CT）图像分析）得出的估计值可与传统的破坏性方法相媲美。为实现这一目标，对两种广泛认可的深度学习架构——U-Net 3D和全卷积网络（FCN）3D进行建模，以分割和分析鸡蛋的三维CT图像。创建并标记了一个真实CT图像数据集，从而能够提取包括高度、宽度、蛋壳厚度和体积在内的重要形态测量数据。这些模型的准确率高达98.69%，与手动测量结果相比，证明了它们的有效性。这些发现凸显了CT图像分析与深度学习相结合在工业和研究环境中作为一种非侵入性替代方法的潜力。这种方法不仅最大限度地减少了对侵入性程序的需求，还为鸡蛋质量评估提供了一种可扩展且可靠的方法。
