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

各种生理信号的自动监测和筛选是现代医学中不可缺少的工具。然而，尽管 对于某些生命信号的长期监测和筛查模式的优势，有相当多的应用 没有可用的自动监测或筛查。例如，需要导尿的患者有很大的风险 尿路感染，但在放置导尿管的情况下对正在发展的感染进行长期监测通常需要护理人员 经常采集尿样，然后必须将其运送到实验室设施进行正在发展的感染检测。颠覆性 无透镜成像、流体学、图像处理、计算机视觉和机器学习相结合的技术提供了巨大的 有机会开发可连接到导尿管的新设备，以自动监测尿路感染。然而，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要在尿样中检测的异常的异质性。 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、开发和测试一种系统 集成了无透镜成像、流体、图像处理、计算机视觉和机器学习，以自动监控尿路感染。 该系统将以尿液样本为输入，在样本流经流体通道时用无透镜显微镜对其成像，重建 使用高级全息重建算法的图像，并使用检测和分类异常，例如白细胞 先进的计算机视觉和机器学习算法。具体地说，本项目将：(1)设计射流和光学硬件以 适当地从患者线上采集尿样，将样本流过无透镜成像仪，并捕捉样本的全息图；(2)开发 基于深网络结构的全息图像重建算法受光物理绕射的限制产生高 从无透镜全息图中获得高质量的样本图像；(3)开发需要最少人工操作的深度学习算法 监督以检测体液样本中可能表明正在发生的感染的各种异常(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一个系统中，以便在尿样上进行验证 根据标准的尿液监测和筛查方法从患者的粪便中获得。 相关性(请参阅说明)： 该项目可能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(大多数 普通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 并将尿样运送到实验室设施进行检测，并实现尿路感染的自动化长期监测和筛查。早些时候 检测到正在发展的尿路感染可以使护理人员在尿路感染进展到需要的时间点之前先发制人地拔出尿管 抗生素治疗，从而减少抗生素的总体使用。该项目将开发的技术也可以用于筛查 其他液体中的异常，如中央脊髓液，以及检测和分类图像中大量细胞的方法可能导致 用于其他计算机视觉应用的大规模多目标检测和跟踪的进展。

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.