High accuracy automated tick classification using computer vision

使用计算机视觉进行高精度自动蜱分类

基本信息

批准号：
10699845
负责人：
Autumn Goodwin
金额：
$ 95.04万
依托单位：
VECTECH, LLC
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-18 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10699845
关键词：
Acceleration Adult Algorithms American Area Bar Codes Classification Collaborations Color Communities Computer Vision Systems Culicidae DNA Data Data Set Databases Development Devices Disease Ensure Female General Population Geography Goals Gravid Health education Human Image Incidence Larva Life Lighting Lyme Disease Marketing Medical Methods Modeling Monitor Morphology Nymph Optics Phase Prevention Process Public Health Quality Control Reporting Research Resolution Resources Scientist Spatial Distribution Specimen Surveillance Methods Surveillance Program System Systems Development Taxonomy Telephone Testing Tick-Borne Diseases Ticks Time Training Translating United States Update Vaccines Vector-transmitted infectious disease Visual Visualization Work advanced system artificial intelligence algorithm classification algorithm commercialization convolutional neural network deep learning design experience fighting high risk human disease improved insight male mobile application prototype response sample collection sex sexual dimorphism tick bite tool transmission process vector vector control vector management strategies vector tick web site

项目摘要

Abstract. The incidence of US tick-borne diseases has more than doubled in the last two decades. Today, Lyme disease is the most common vector-borne disease in the United States, impacting over half-a-million Americans each year. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites and early tick bite treatment is the primary focus of disease mitigation. Tick vector surveillance—monitoring an area to understand tick species composition, abundance, and spatial distribution—is key to providing the public with accurate and up-to-date information when they are in areas of high risk, and enabling precision vector control when necessary. Despite the importance of vector surveillance, current practices are highly resource intensive and require significant labor and time to collect and identify vector specimens. Acarologist or field taxonomist expertise is a limited resource required for tick identification, creating a significant capability barrier for national tick surveillance practice. While mobile applications to facilitate passive surveillance and reporting of human-tick encounters have grown in popularity, variable image quality, limited engagement, and scientist misidentification of rare, invasive, or morphologically similar tick species hinder the scalability of this approach. To date, no automated solutions exist to build tick identification capacity. We seek to advance Phase I work that successfully achieved an imaging and automated identification system capable of instantaneously and accurately identifying twelve adult tick species with 98% accuracy. This proposal will first improve the Phase I optical design for scalability to accommodate imaging of additional intra-specific tick species variability as nymphs, adult males, and unfed or engorged adult females. In parallel, we develop methods to optimize quality of guided user imaging of ticks in a mobile app approach for the general public. This will enable the development of a representative image database with partners including TickSpotters, TickCheck, the Walter Reed Biosystems Unit (WRBU), and others. The resulting database will be used to train, validate, test and deploy high-accuracy computer vision models in two tick identification products for professional public health and the general public. Ultimately the approaches developed here will enable vector management organizations to leverage image recognition in a practical system that will increase capacity and capability for biosurveillance, and equip the general public with improved tools to identify ticks during a human-tick encounter.

摘要：在过去的二十年里，美国蜱传疾病的发病率增加了一倍多。莱姆病是美国最常见的媒介传播疾病，影响超过每年有 50 万美国人由于缺乏有效的蜱传疾病疫苗而无法预防。蜱虫叮咬和早期蜱虫叮咬治疗是缓解蜱虫媒介疾病的主要焦点。监测——监测一个区域以了解蜱的物种组成、丰度和空间分发——是向公众提供准确和最新信息的关键尽管矢量很重要，但在必要时启用精确的矢量控制。监督，目前的做法是高度资源密集型的，需要大量的劳动力和时间收集和识别媒介标本。蜱虫学家或现场分类学家的专业知识是有限的资源。蜱虫识别所需，为国家蜱虫监测造成重大能力障碍移动应用程序促进了人类蜱虫的被动监测和报告。遭遇越来越受欢迎，图像质量参差不齐，参与度有限，科学家对稀有、入侵或形态相似的蜱种的错误识别阻碍了这种方法的可扩展性迄今为止，我们还没有建立自动化解决方案来提高蜱虫识别能力。第一阶段工作成功实现了成像和自动识别系统，能够该提案可即时准确地识别 12 种成年蜱虫，准确率达 98%。将首先改进第一阶段的光学设计，以实现可扩展性，以适应额外的成像种内蜱种变异，如若虫、成年雄性和未进食或饱食的成年雌性。与此同时，我们开发了方法来优化移动应用程序中蜱虫引导用户成像的质量这将使开发具有代表性的图像数据库成为可能。合作伙伴包括 TickSpotters、TickCheck、Walter Reed Biosystems Unit (WRBU) 等。由此产生的数据库将用于训练、验证、测试和部署高精度计算机视觉模型两种蜱虫识别产品最终适用于专业公共卫生和普通大众。这里开发的方法将使媒介管理组织能够利用图像识别一个实用的系统，将提高生物监测的能力和能力，并装备一般人向公众提供改进的工具，以在人类蜱虫遭遇期间识别蜱虫。