Computer Vision for Malaria Microscopy: Automated Detection and Classification of Plasmodium for Basic Science and Pre-Clinical Applications

项目摘要/摘要 在世界三大传染病中，疟疾因疟原虫的复杂性而脱颖而出。 生命周期和生物学。疟疾寄生虫主要在红细胞内繁殖，在它们的整个生命周期中有 蛋白质表达和代谢的戏剧性变化，改变了它们的外表、行为和易感性 由宿主免疫系统或抗疟疾药物清除。因为这是血液的感染，活组织检查 只需刺一下手指，就能通过光获得组织病理学信息 显微镜是研究并最终控制和治疗疟疾的关键工具。手动审查是 刻苦和不完美。基于神经网络的计算机视觉(CV)方法可以加速数据 从光学显微镜获取数据，并创新提取数据的新方法，目前只能通过 昂贵、劳动密集型的台式分子方法或少数疟疾患者耗时的审查 拥有必要培训和经验的显微镜专家，能够分辨出 寄生虫的形式。 这份R21提案建立在为期12个月的准备工作的基础上，并得到了Johns的试点拨款的支持 霍普金斯大学数据密集型工程与科学学院的合作追求 医学与工程学专业。联合首席调查员开发了一种基于深度学习的CV算法 在&gt；10,000张恶性疟原虫环期寄生虫的公共数据集上进行训练，这些图像可以检测到 并以0.97的准确度对寄生虫进行量化。然而，更多的信息已经成熟，可以从其中提取 疟疾涂片超越了简单的寄生虫检测。我们建造了第二代简历的早期原型 算法能够识别正确的寄生虫阶段到早期、中期或晚期的环状阶段 &gt；0.80精度，在本提案中，我们的目标是改进性能并扩展 疟疾CV系统得到更广泛的应用，同时在多个领域开创新的计算方法 自适应和弱监督和半监督学习。 拟议的项目将导致开发下一代疟疾CVS系统，该系统可以 从Brightfield图像中提取分子数据，供医生或临床研究人员使用。我们将建造 开发原型CV系统以优化性能，开发高阶分类器(例如，区分 从不活的循环寄生虫中存活，发现一次感染的细胞对迟发性溶血的预后 在治疗之后)，并针对不同的组织背景(例如，肝脏、脾)运行该算法。的产品 这项工作将是一个尖端的基于神经网络的疟疾CV系统，提供多路读出 寄生虫生物参数和细胞病理学有助于推动疟疾研究和 生物医学CV分析向前推进。

PROJECT SUMMARY/ABSTRACT Among the “big three” infectious diseases worldwide, malaria stands out for the complexity of the Plasmodium life-cycle and biology. Malaria parasites breed mainly within red blood cells, and across their lifespan there are dramatic shifts in protein expression and metabolism that alter their appearance, behavior, and susceptibility to clearance by the host immune system or antimalarial drugs. Because it is an infection of the blood, a biopsy can be taken with a simple finger prick, and the ability to derive histopathological information via light microscopy is a critical tool in the study of, and ultimately control and treatment of, malaria. Manual review is painstaking and imperfect. Neural network-based computer vision (CV) approaches can accelerate data acquisition from light microscopy and innovate new methods of extracting data currently only possible through costly, labor-intensive benchtop molecular methods or time-consuming review by a small number of malaria microscopy experts with the necessary training and experience to distinguish subtle differences between parasite forms. This R21 proposal builds on 12 months of preparatory work supported by a pilot grant from The Johns Hopkins University Institute for Data Intensive Engineering and Science, a collaborative pursuit of the Schools of Medicine and Engineering. The co-principal investigators developed a deep learning-based CV algorithm trained on a public dataset of >10,000 images of Plasmodium falciparum ring stage parasites that can detect and quantify parasites with >0.97 accuracy. However, significantly more information is ripe for extraction from malaria smears beyond the simple detection of parasites. We built an early prototype of a 2nd-generation CV algorithm capable of identifying the correct parasite stage to the level of early, middle or late ring stage with >0.80 accuracy, and in this proposal we aim to refine the performance and extend the capabilities of the malaria CV system to wider applications while pioneering new computational methods in multiple domain adaptation and weakly- and semi-supervised learning. The proposed project would result in the development of a next-generation malaria CV system that can derive molecular data from brightfield images for use by investigators at the bench or in the clinic. We will build out the prototype CV system to optimize performance, develop higher-order classifiers (e.g., differentiating viable from nonviable circulating parasites, finding once-infected cells for the prognosis of delayed hemolysis after treatment), and run the algorithm against different tissue backgrounds (e.g., liver, spleen). The product of this work will be a cutting-edge neural network-based malaria CV system that provides a multiplex readout of parasite biological parameters and cellular pathology to help propel the fields of malaria research and biomedical CV analysis forward.