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

项目总结/摘要 在全球“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，在它们的一生中， 蛋白质表达和代谢的巨大变化，改变了它们的外观，行为和对 通过宿主免疫系统或抗疟药物清除。因为这是血液感染，活组织检查 可以通过一个简单的手指针刺，并通过光获得组织病理信息的能力， 显微镜是研究疟疾并最终控制和治疗疟疾的关键工具。人工审核是 辛苦而不完美基于神经网络的计算机视觉（CV）方法可以加速数据处理 从光学显微镜采集和创新的新方法提取数据目前只能通过 昂贵的、劳动密集型的台式分子方法或由少数疟疾患者进行耗时的审查 显微镜专家与必要的培训和经验，以区分之间的细微差异 寄生虫形成。 这项R21提案建立在12个月的准备工作的基础上，由约翰的试点赠款支持 霍普金斯大学数据密集型工程和科学研究所，学校的合作追求 医学与工程系联合主要研究人员开发了一种基于深度学习的CV算法 在包含> 10，000张恶性疟原虫环期寄生虫图像的公共数据集上进行训练， 并以>0.97的准确度定量寄生虫。然而，更多的信息是成熟的提取， 疟疾涂片不仅仅是检测寄生虫。我们建造了第二代CV的早期原型， 算法能够识别正确的寄生虫阶段的水平，早期，中期或后期环阶段， >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，以更广泛的应用，同时开拓新的计算方法，在多个领域 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CV系统， 从明场图像中获得分子数据，供研究人员在实验室或诊所使用。我们将建立 原型CV系统以优化性能，开发高阶分类器（例如，区分 从不能存活的循环寄生虫中存活，发现一次感染的细胞用于延迟溶血的预后 治疗后），并针对不同的组织背景（例如，肝、脾）。的产物 这项工作将是一个先进的基于神经网络的疟疾CV系统，它提供了一个多路读出， 寄生虫生物学参数和细胞病理学，以帮助推动疟疾研究领域， 生物医学CV分析向前。

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.