In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin-embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue-based analyses.

中文翻译：

---

用于量化组织学标本颜色特征的数字病理学工具


在临床前研究中，组织样本的组织学分析通常仅限于定性或半定量评分评估。即使由经验丰富的病理学家阅读，这些方法的主观性也会损害该分析的可靠性。此外，手动图像评估的费力性质通常导致分析仅限于相对少量的图像，而这些图像可能无法准确地代表整个样本。因此，显然需要自动化图像分析工具，能够对石蜡包埋或冷冻保存组织的组织学样本进行稳健且快速的定量。为了满足这一需求，我们开发了一种彩色图像分析算法（DigiPath）来量化组织学切片中的不同颜色特征。我们展示了该工具在多种类型的组织样本和病理特征中的实用性，并将我们的程序的结果与其他定量方法（例如颜色阈值和手部追踪）进行比较。我们相信，该工具将使组织学样本的表征更加彻底和可靠，从而促进基于组织的分析具有更好的严谨性和可重复性。