U.S. National Library of Medicine, National Institutes of Health, Bethesda, Maryland.
Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
Transl Res. 2018 Apr;194:36-55. doi: 10.1016/j.trsl.2017.12.004. Epub 2018 Jan 12.
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.
疟疾仍然是全球卫生的主要负担，全球约有 2 亿例病例，每年有超过 40 万人死亡。除了生物医学研究和政治努力外，现代信息技术在许多抗击疾病的尝试中发挥着关键作用。降低死亡率的一个障碍是疟疾诊断不充分。为了改善诊断，已经使用图像分析软件和机器学习方法来量化显微镜血片中的寄生虫载量。本文概述了这些技术，并讨论了用于显微镜疟疾诊断的图像分析和机器学习的当前发展。我们根据用于成像、图像预处理、寄生虫检测和细胞分割、特征计算以及自动细胞分类的技术，按照不同的方法对发表的文献进行了分类。读者可以在表中找到列出的不同技术，并在旁边列出相关文章，包括薄血涂片和厚血涂片图像。我们还讨论了深度学习和智能手机技术在未来疟疾诊断方面的最新进展。
