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

用于疟疾显微镜的计算机视觉：用于基础科学和临床前应用的疟原虫自动检测和分类

• 批准号: 10576701
• 负责人: Benjamin D Haeffele
• 金额: $ 23.15万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576701
• 关键词: Acceleration; Address; Africa South of the Sahara; African; Aftercare; Algorithms; Antimalarials; Appearance; Artificial Intelligence; Basic Science; Behavior; Biological; Biology; Biomedical Engineering; Biopsy; Blood; Breeding; Brightfield Microscopy; Cause of Death; Cells; Cessation of life; Child; Classification; Clinic; Clinical; Collaborations; Color; Communicable Diseases; Computer Vision Systems; Computing Methodologies; Consumption; Data; Data Set; Data Sources; Derivation procedure; Detection; Development; Devices; Disease; Drug Exposure; Drug resistance; E-learning; Engineering; Equipment; Erythrocytes; Film; Fingers; Funding Mechanisms; Generations; Genetic Transcription; Grant; Hemolysis; Histologic; Histopathology; Image; Image Analysis; Imaging problem; Immune system; Infection; International; Label; Laboratories; Life Cycle Stages; Liver; Longevity; Machine Learning; Malaria; Manuals; Methods; Microscopy; Modeling; Modernization; Molecular; Monitor; Network-based; Outcome; Parasites; Parasitology; Pathology; Performance; Plasmodium; Plasmodium falciparum; Population; Prediction of Response to Therapy; Predisposition; Preparation; Principal Investigator; Process; Prognosis; Public Health; Reproducibility; Research; Research Institute; Research Personnel; Resolution; Running; Science; Semantics; Sepsis; Site; Slide; Specimen; Spleen; Stains; Surface; Surveys; Techniques; Technology; Time; Tissues; Training; Treatment Efficacy; Universities; Variant; Visual; Work; algorithm training; biomedical imaging; cell injury; cellular pathology; cost; data acquisition; deep learning; deep neural network; design; detection platform; digital; experience; innovation; learning strategy; light microscopy; medical schools; microscopic imaging; miniaturize; neural network; next generation; novel; pre-clinical; preservation; protein expression; protein metabolism; prototype; statistics; success; supervised learning; tool; visual information

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.

项目摘要/摘要 在全球“三巨头”传染病中，疟疾脱颖而出，质量复杂 生命周期和生物学。疟疾寄生虫主要繁殖在红细胞中，在其寿命中有 蛋白质表达和新陈代谢的急剧转变改变了其外观，行为和易感性 宿主免疫系统或抗疟药的清除。因为它是血液的感染，所以活检 可以用简单的手指刺和通过光得出组织病理学信息的能力 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 艰苦而不完美。基于神经网络的计算机视觉（CV）方法可以加速数据 从光学显微镜和创新的新方法获取目前可以通过 昂贵的，劳动密集型的台式分子方法或少量疟疾的耗时评论 具有必要培训和经验的显微镜专家，以区分微妙的差异 寄生虫形式。 该R21提案建立在约翰斯的飞行员赠款支持12个月的准备工作中 霍普金斯大学数据密集工程与科学研究所，对学校的合作追求 医学和工程。联合首席研究人员开发了一种基于深度学习的简历算法 在公共数据集上训练了> 10,000张疟原虫圆环舞台寄生虫的图像 并以> 0.97的精度量化寄生虫。但是，从中有明显的信息已经成熟 疟疾超出了对寄生虫的简单检测。我们建立了第二代简历的早期原型 算法能够将正确的寄生虫阶段识别为早期，中环或晚期阶段的水平 > 0.80的准确性，在此提案中，我们旨在完善性能并扩展功能 在多个领域开创新的计算方法的同时，疟疾简历系统到广泛的应用 适应和弱和半监督的学习。 拟议的项目将导致开发下一代疟疾简历系统，可以 从Brightfield图像中得出分子数据，以供工作人员在长凳或诊所中使用。我们将建造 删除原型简历系统以优化性能，开发高阶分类器（例如，区分 可依赖于不可生存的循环寄生虫，发现曾经感染的细胞用于预后延迟溶血 治疗后），并在不同的组织背景（例如肝，囊）上运行算法。产品的产物 这项工作将是一个尖端的基于神经网络的疟疾简历系统，可提供多重读数 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 生物医学简历分析前进。