Digitized images of herbarium specimens are highly diverse with many potential sources of visual noise and bias. The systematic removal of noise and minimization of bias must be achieved in order to generate biological insights based on the plants rather than the digitization and mounting practices involved. Here, we develop a workflow and data set of high‐resolution image masks to segment plant tissues in herbarium specimen images and remove background pixels using deep learning.

中文翻译：

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使用深度学习生成植物标本的分割掩模和用于训练分割模型的数据集。


植物标本室标本的数字化图像高度多样化，存在许多潜在的视觉噪声和偏差来源。必须实现系统地消除噪音和最小化偏差，以便基于植物而不是所涉及的数字化和安装实践产生生物学见解。在这里，我们开发了高分辨率图像掩模的工作流程和数据集，以分割植物标本图像中的植物组织并使用深度学习去除背景像素。