Ong Song-Quan, Pinoy Nathan, Lim Min Hui, Bjerge Kim, Peris-Felipo Francisco Javier, Lind Rob, Cuff Jordan P, Cook Samantha M, Høye Toke Thomas
Department of Ecoscience, Aarhus University, C. F. Møllers Allé 8, DK-8000, Aarhus, Denmark.
Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
Curr Res Parasitol Vector Borne Dis. 2025 May 8;7:100268. doi: 10.1016/j.crpvbd.2025.100268. eCollection 2025.
Computer vision methods offer great potential for rapid image-based identification of medically important arthropod specimens. However, imaging large numbers of specimens is time consuming, and it is difficult to achieve the high image quality required for machine learning models. Conventional imaging methods for identifying and digitizing arthropods, such as insects and spiders, use a stereomicroscope or macro lenses with a camera. This method is challenging due to the narrow field of view, especially when large numbers of arthropods need to be processed. In this paper, we present a high-throughput scanner-based method for capturing images of arthropods that can be used to generate large datasets suitable for training machine learning algorithms for identification. We demonstrate the ability of this approach to image arthropod samples collected with different sampling methods, such as sticky traps (unbaited, in different colors), baited mosquito traps as used by the US Centers for Disease Control and Prevention (CDC) and BioGents-Sentinel (BGS), and UV light traps with a sticky pad. Using different strategies to place the arthropods on a charge-coupled device (CCD) flatbed scanner and optimized settings that balance processing time and image quality, we captured high-resolution images of various arthropods and obtained morphological details with resolution and magnification similar to a stereomicroscope. We validate the method by comparing the performance of three different deep learning models (InceptionV3, ResNet and MobileNetV2) on two different datasets, namely the scanned images from this study and the images captured with a camera of a stereomicroscope. The results show that the performance of the models trained on the two datasets is not significantly different, indicating that the quality of the scanned images is comparable to that of a stereomicroscope.
计算机视觉方法在基于图像快速识别具有医学重要性的节肢动物标本方面具有巨大潜力。然而，对大量标本进行成像非常耗时，并且难以达到机器学习模型所需的高图像质量。用于识别和数字化节肢动物（如昆虫和蜘蛛）的传统成像方法使用带有相机的立体显微镜或微距镜头。由于视野狭窄，这种方法具有挑战性，特别是在需要处理大量节肢动物时。在本文中，我们提出了一种基于高通量扫描仪的方法来捕获节肢动物图像，该方法可用于生成适合训练用于识别的机器学习算法的大型数据集。我们展示了这种方法对用不同采样方法收集的节肢动物样本进行成像的能力，这些采样方法包括粘性诱捕器（未诱饵、不同颜色）、美国疾病控制与预防中心（CDC）和BioGents-Sentinel（BGS）使用的诱饵捕蚊器以及带有粘性垫的紫外线诱捕器。通过使用不同策略将节肢动物放置在电荷耦合器件（CCD）平板扫描仪上，并优化设置以平衡处理时间和图像质量，我们捕获了各种节肢动物的高分辨率图像，并获得了与立体显微镜相似的分辨率和放大倍数的形态细节。我们通过比较三种不同深度学习模型（InceptionV3、ResNet和MobileNetV2）在两个不同数据集上的性能来验证该方法，这两个数据集分别是本研究的扫描图像和用立体显微镜相机捕获的图像。结果表明，在这两个数据集上训练的模型性能没有显著差异，这表明扫描图像的质量与立体显微镜的质量相当。
