Background

Millions of people throughout the world suffer from parasite infections. Traditionally, technicians use manual eye inspection of microscopic specimens to perform a parasite examination. However, manual operations have limitations that hinder the ability to obtain precise egg counts and cause inefficient identification of infected parasites on co-infections. The technician requirements for handling a large number of microscopic examinations in countries that have limited medical resources are substantial. We developed the helminth egg analysis platform (HEAP) as a user-friendly microscopic helminth eggs identification and quantification platform to assist medical technicians during parasite infection examination.

Methods

Multiple deep learning strategies including SSD (Single Shot MultiBox Detector), U-net, and Faster R-CNN (Faster Region-based Convolutional Neural Network) are integrated to identify the same specimen allowing users to choose the best predictions. An image binning and egg-in-edge algorithm based on pixel density detection was developed to increase the performance. Computers with different operation systems can be gathered to lower the computation time using our easy-to-deploy software architecture.

Results

A user-friendly interface is provided to substantially increase the efficiency of manual validation. To adapt to low-cost computers, we architected a distributed computing structure with high flexibilities.

Conclusions

HEAP serves not only as a prediction service provider but also as a parasitic egg database of microscopic helminth egg image collection, labeling data and pretrained models. All images and labeling resources are free and accessible at http://heap.cgu.edu.tw. HEAP can also be an ideal education and training resource for helminth egg examination.

中文翻译：

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蠕虫卵分析平台（HEAP）：基于深度学习架构集成的微观蠕虫卵识别和量化开放平台

背景

全世界有数百万人患有寄生虫感染。传统上，技术人员使用人工目视检查显微标本来进行寄生虫检查。然而，手动操作存在限制，阻碍了获得精确的卵计数的能力，并导致在共同感染时对受感染寄生虫的识别效率低下。在医疗资源有限的国家，处理大量显微镜检查的技术人员要求非常高。我们开发了蠕虫卵分析平台（HEAP）作为一个用户友好的微观蠕虫卵识别和量化平台，以协助医疗技术人员进行寄生虫感染检查。

方法

集成了多种深度学习策略，包括 SSD（Single Shot MultiBox Detector）、U-net 和 Faster R-CNN（Faster R-CNN（Faster Region-based Convolutional Neural Network），以识别同一样本，让用户选择最佳预测。开发了一种基于像素密度检测的图像分箱和egg-in-edge算法以提高性能。使用我们易于部署的软件架构，可以收集具有不同操作系统的计算机以降低计算时间。

结果

提供了一个用户友好的界面，以显着提高手动验证的效率。为了适应低成本的计算机，我们构建了一个具有高灵活性的分布式计算结构。

结论

HEAP 不仅作为预测服务提供商，而且作为寄生虫卵数据库，包括微观蠕虫卵图像收集、标记数据和预训练模型。所有图像和标签资源都是免费的，可在 http://heap.cgu.edu.tw 访问。HEAP 也可以成为蠕虫卵检查的理想教育和培训资源。