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

自动监测和筛选各种生理信号是现代医学中不可或缺的工具。但尽管 由于某些生命信号的长期监测和筛查模式的优势， 其中没有自动监控或筛选可用。例如，需要导尿的患者有很大的风险 尿路感染，但长期监测发展中的感染，而导尿管是在地方通常需要护理人员， 经常收集尿样，然后必须将尿样运送到实验室设施以测试发展中的感染。颠覆性 无透镜成像、射流、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 这是一个机会，可以开发新的设备，可以连接到导尿管，以自动监测尿路感染。然而，小说 需要图像重建、对象检测和分类以及深度学习算法来应对低图像分辨率等挑战， 分离度、有限的标记数据和尿样中待检测异常的异质性。 该项目汇集了计算机科学家，工程师和临床医生的多学科团队，设计，开发和测试系统， 它集成了无透镜成像、射流、图像处理、计算机视觉和机器学习，可自动监测尿路感染。 该系统将尿液样本作为输入，当样本流过流体通道时，用无透镜显微镜对样本进行成像， 使用先进的全息重建算法的图像，并检测和分类异常，例如，白色血细胞，使用 先进的计算机视觉和机器学习算法。具体而言，本项目将：（1）设计流体和光学硬件， 适当地从患者管线中取样尿液，使样品流过无透镜成像器，并捕获样品的全息图;（2）显影 基于受光衍射物理学约束的深度网络架构的全息图像重建算法， 从无透镜全息图中获得样本的高质量图像;（3）开发深度学习算法， 监测以检测可能指示正在发展的感染的流体样品中的各种异常（例如，白色的存在 血液细胞或细菌）;以及（4）将上述硬件和软件开发集成到待在尿液样本上验证的系统中 从患者丢弃物中获得，与标准尿液监测和筛查方法进行比较。 相关性（参见说明）： 该项目可能会导致开发一种低成本的设备，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），并且这样的设备可以消除患者或护理人员手动收集 尿液样本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UTI筛查。早期 检测到正在发展的UTI可以允许护理人员在UTI进展到需要的程度之前抢先移除导管。 抗生素治疗，从而减少整体抗生素的使用。本项目开发的技术也可用于筛选 其他液体（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 在其他计算机视觉应用中的大规模多目标检测和跟踪方面取得了进展。

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.