Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
Comput Biol Med. 2023 Sep;163:107203. doi: 10.1016/j.compbiomed.2023.107203. Epub 2023 Jun 28.
Diagnosing gastrointestinal parasites by microscopy slide examination often leads to human interpretation errors, which may occur due to fatigue, lack of training and infrastructure, presence of artifacts (e.g., various types of cells, algae, yeasts), and other reasons. We have investigated the stages in automating the process to cope with the interpretation errors. This work presents advances in two stages focused on gastrointestinal parasites of cats and dogs: a new parasitological processing technique, named TF-Test VetPet, and a microscopy image analysis pipeline based on deep learning methods. TF-Test VetPet improves image quality by reducing cluttering (i.e., eliminating artifacts), which favors automated image analysis. The proposed pipeline can identify three species of parasites in cats and five in dogs, distinguishing them from fecal impurities with an average accuracy of 98,6%. We also make available the two datasets with images of parasites of dogs and cats, which were obtained by processing fecal smears with temporary staining using TF-Test VetPet.
通过显微镜幻灯片检查诊断胃肠道寄生虫病常常导致人为解释错误，这些错误可能是由于疲劳、缺乏培训和基础设施、存在伪影（例如，各种类型的细胞、藻类、酵母）等原因引起的。我们已经研究了自动化处理过程中的各个阶段，以应对解释错误。这项工作在两个阶段取得了进展，重点是猫和狗的胃肠道寄生虫：一种新的寄生虫学处理技术，命名为 TF-Test VetPet，以及基于深度学习方法的显微镜图像分析管道。TF-Test VetPet 通过减少混乱（即消除伪影）来提高图像质量，这有利于自动化图像分析。所提出的管道可以识别猫的三种寄生虫和狗的五种寄生虫，将它们与粪便杂质区分开来，平均准确率为 98.6%。我们还提供了使用 TF-Test VetPet 对粪便涂片进行临时染色处理获得的狗和猫寄生虫图像的两个数据集。
