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

基本信息

批准号：
10408071
负责人：
Benjamin D Haeffele
金额：
$ 27.46万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-30 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10408071
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

项目摘要

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不可或缺的工具。然而，尽管由于某些生命信号的长期监测和筛查方式占主导地位，因此有大量应用没有可用的自动监控或筛查。例如，需要导尿的患者面临着巨大的风险：尿路感染，但在留有导尿管时长期监测正在发生的感染通常需要护理人员经常收集尿液样本，然后必须将其运送到实验室设施以检测是否存在感染。破坏性的无透镜成像、流体学、图像处理、计算机视觉和机器学习的交叉技术提供了巨大的有机会开发可连接到导尿管以自动监测尿路感染的新设备。不过，小说需要图像重建、目标检测和分类以及深度学习算法来应对低图像等挑战分辨率、有限的标记数据以及尿液样本中检测到的异常的异质性。该项目汇集了由计算机科学家、工程师和临床医生组成的多学科团队，设计、开发和测试一个系统，该系统集成了无透镜成像、流体学、图像处理、计算机视觉和机器学习，可自动监测尿路感染。该系统将以尿液样本作为输入，当样本流经流体通道时用无透镜显微镜对样本进行成像，重建使用先进的全息重建算法处理图像，并使用以下方法检测和分类异常情况，例如白细胞先进的计算机视觉和机器学习算法。具体来说，该项目将：（1）设计流体和光学硬件从患者导管中适当取样尿液，使样本流过无透镜成像仪，并捕获样本的全息图； (2) 开发基于受光衍射物理约束的深层网络架构的全息图像重建算法，可产生高来自无透镜全息图的样本的高质量图像； (3) 开发需要最低限度手动操作的深度学习算法监督以检测液体样本中可能表明正在发生感染的各种异常情况（例如，存在白色血细胞或细菌）； (4) 将上述硬件和软件开发集成到一个系统中，以对尿液样本进行验证根据标准尿液监测和筛查方法从患者丢弃物中获得。相关性（参见说明）：该项目可能会导致开发一种低成本设备，用于自动筛查和监测尿路感染（最常见的尿路感染）常见的医院和疗养院获得性感染），这样的设备可以消除患者或护理人员手动收集的需要尿液样本并将其运送到实验室设施进行测试，并实现尿路感染的自动长期监测和筛查。早期的检测正在发生的尿路感染可以让护理人员在尿路感染发展到需要治疗的程度之前抢先拔除导管抗生素治疗，从而减少抗生素的总体使用。该项目开发的技术也可用于筛选其他液体（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会导致其他计算机视觉应用的大规模多目标检测和跟踪方面的进展。