SCH: A Computer Vision and Lens-Free Imaging System for Automatic Monitoring of Infections

SCH：用于自动监测感染的计算机视觉和无镜头成像系统

• 批准号: 10162472
• 负责人: Benjamin D Haeffele
• 金额: $ 28.31万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162472
• 关键词: Algorithms; Antibiotic Therapy; Antibiotics; Bacteria; Bacteriuria; Caregivers; Catheters; Cations; Cells; Cerebrospinal Fluid; Classification; Clinical; Computer Vision Systems; Computers; Data; Detection; Development; Devices; Diagnostic; Diffusion; Early Diagnosis; Engineering; Erythrocytes; Evaluation; Goals; Hospital Nursing; Image; Infection; Instruction; Knowledge; Label; Laboratories; Lead; Leukocytes; Light; Lighting; Liquid substance; Machine Learning; Manuals; Maps; Measurement; Methods; Microscope; Modality; Modern Medicine; Monitor; Nursing Homes; Optics; Patients; Performance; Physics; Physiological; Prevalence; Principal Investigator; Procedures; Process; Resistance; Resolution; Risk; Sampling; Scientist; Signal Transduction; Specimen; Supervision; Surface; System; Technology; Testing; Training; Urinalysis; Urinary Catheterization; Urinary tract infection; Urine; base; biological heterogeneity; classification algorithm; cost; deep learning algorithm; design; diffraction of light; heterogenous data; hologram; image processing; image reconstruction; imager; imaging system; laboratory facility; lens; machine learning algorithm; multidisciplinary; network architecture; novel; particle; reconstruction; screening; software development; tool; urinary

Automated monitoring and screening of various physiological signals is an indispensable tool in modern medicine. However, despite the preponderance of long-term monitoring and screening modalities for certain vital signals, there are a significant number of applications for which no automated monitoring or screening is available. For example, patients in need of urinary catheterization are at significant risk of urinary tract infections, but long-term monitoring for a developing infection while a urinary catheter is in place typically requires a caregiver to frequently collect urine samples which then must be transported to a laboratory facility to be tested for a developing infection. Disruptive technologies at the intersection of lens-free imaging, fluidics, image processing, computer vision and machine learning offer a tremendous opportunity to develop new devices that can be connected to a urinary catheter to automatically monitor urinary tract infections. However, novel image reconstruction, object detection and classification, and deep learning algorithms are needed to deal with challenges such as low image resolution, limited labeled data, and heterogeneity of the abnormalities to be detected in urine samples. This project brings together a multidisciplinary team of computer scientists, engineers and clinicians to design, develop and test a system that integrates lens-free imaging, fluidics, image processing, computer vision and machine learning to automatically monitor urinary tract infections. The system will take a urine sample as an input, image the sample with a lens-free microscope as it flows through a fluidic channel, reconstruct the images using advanced holographic reconstruction algorithms, and detect and classify abnormalities, e.g., white blood cells, using advanced computer vision and machine learning algorithms. Specifically, this project will: (1) design fluidic and optical hardware to appropriately sample urine from patient lines, flow the sample through the lens-free imager, and capture holograms of the sample; (2) develop holographic image reconstruction algorithms based on deep network architectures constrained by the physics of light diffraction to produce high quality images of the specimen from the lens-free holograms; (3) develop deep learning algorithms requiring a minimal level of manual supervision to detect various abnormalities in the fluid sample that might be indicative of a developing infection (e.g., the presence of white bloods cells or bacteria); and (4) integrate the above hardware and software developments into a system to be validated on urine samples obtained from patient discards against standard urine monitoring and screening methods. RELEVANCE (See instructions): This project could lead to the development of a low-cost device for automated screening and monitoring of urinary tract infections (the most common hospital and nursing home acquired infection), and such a device could eliminate the need for patients or caregivers to manually collect urine samples and transport them to a laboratory facility for testing and enable automated long-term monitoring and screening for UTIs. Early detection of developing UTIs could allow caregivers to preemptively remove the catheter before the UTI progressed to the point of requiring antibiotic treatment, thus reducing overall antibiotic usage. The technology to be developed in this project could also be used for screening abnormalities in other fluids, such as central spinal fluid, and the methods to detect and classify large numbers of cells in an image could lead to advances in large scale multi-object detection and tracking for other computer vision applications.

各种生理信号的自动监测和筛查是现代医学中必不可少的工具。但是，尽管有 对于某些重要信号的长期监控和筛选方式的优势，有大量申请 没有自动监视或筛选。例如，需要尿导管插入术的患者有明显的风险 尿路感染，但在尿管中进行的长期监测通常需要护理人员 经常收集尿液样品，然后必须将其运送到实验室设施以进行发育中的感染进行测试。破坏性 无镜头成像，流体，图像处理，计算机视觉和机器学习的交集的技术提供了巨大的 开发可以连接到尿管导管以自动监测尿路感染的新设备的机会。但是，新颖 需要图像重建，对象检测和分类以及深度学习算法来应对诸如低图像之类的挑战 分辨率，有限的标记数据以及在尿液样品中要检测到的异常的异质性。 该项目汇集了一个由计算机科学家，工程师和临床医生组成的多学科团队，以设计，开发和测试一个系统 集成无镜头的成像，流体，图像处理，计算机视觉和机器学习，以自动监测尿路感染。 该系统将采用尿液样本作为输入，在流经流体通道时，用无镜头显微镜对样品进行图像，重建 使用高级全息重建算法的图像，并检测和分类异常，例如白细胞，使用使用 先进的计算机视觉和机器学习算法。具体而言，该项目将：（1）设计流体和光学硬件 适当地从患者品系中采样尿液，将样品流到无镜头成像仪中，并捕获样品的全息图； （2）发展 全息图像重建算法基于由光衍射的物理学约束的深网架构，产生较高的 来自无镜头全息图的标本的优质图像； （3）开发深度学习算法，需要最少的手册水平 监督以检测流体样本中各种异常的监督，这些异常可能表明感染发生（例如，白色的存在 血细胞或细菌）； （4）将上述硬件和软件开发集成到一个系统中，以在尿液样本上进行验证 从患者丢弃的标准尿液监测和筛查方法中获得。 相关性（请参阅说明）： 该项目可能会导致开发低成本设备，用于自动筛查和监测尿路感染（最多的。 普通医院和疗养院获得感染），这种设备可以消除患者或护理人员手动收集的需求 尿液样品并将其运送到实验室设施进行测试，并实现自动化的长期监测和筛查UTI。早期的 检测开发的UTI可以使护理人员在UTI发展之前先进行避免导管 抗生素处理，从而减少了总体抗生素使用情况。该项目中要开发的技术也可以用于筛选 其他液体（例如中央脊髓液）的异常，以及检测和分类图像中大量细胞的方法可能导致 大规模进步的多对象检测和跟踪其他计算机视觉应用程序。