In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
翻译:在许多研究领域,通过多学科访问图像数据及其跨学科说明加快了科学进步。然而,跟踪这些说明以确保高质量的多用途数据集是一项艰巨和劳动密集型的任务。我们开发了开放源码在线平台EXACT(Expert Algorithm合作工具),以便能够对不同领域在线和离线图像进行跨学科合作分析。EXACT支持多igapixel医学整部幻灯片图像以及图像系列。软件使用一个灵活的插件系统,可以适应多种应用,如以筛选模式计算线形图,在新的验证视图中找到虚假说明,或使用最新的深层学习图像分析技术。这与版本控制系统相结合,可以跟踪数据集的变化,例如,将深层学习实验的结果与特定数据集版本联系起来。exACT可以自由使用,并已成功地应用于广泛的注解任务,包括高度多样化的应用,如以筛选模式计数的深度学习支持的鲸细胞细胞学评分,在跨学科多中心图像集中进行。高清晰的镜像化。