Chromogenic RNAscope dye and haematoxylin staining of cancer tissue facilitates diagnosis of the cancer type and subsequent treatment, and fits well into existing pathology workflows. However, manual quantification of the RNAscope transcripts (dots), which signify gene expression, is prohibitively time consuming. In addition, there is a lack of verified supporting methods for quantification and analysis. This paper investigates the usefulness of gray level texture features for automatically segmenting and classifying the positions of RNAscope transcripts from breast cancer tissue. Feature analysis showed that a small set of gray level features, including Gray Level Dependence Matrix and Neighbouring Gray Tone Difference Matrix features, were well suited for the task. The automated method performed similarly to expert annotators at identifying the positions of RNAscope transcripts, with an F1-score of 0.571 compared to the expert inter-rater F1-score of 0.596. These results demonstrate the potential of gray level texture features for automated quantification of RNAscope in the pathology workflow.

翻译：显色RNAscope染料和乳腺癌组织的苏木精染色有助于诊断癌症类型及后续治疗，并能很好地融入现有病理工作流程。然而，对指示基因表达的RNAscope转录本（点状信号）进行人工定量分析极为耗时。此外，目前缺乏经过验证的辅助定量分析方法。本文研究了灰度纹理特征在自动分割和分类乳腺癌组织RNAscope转录本位置中的有效性。特征分析表明，包括灰度依赖矩阵和邻域灰度差分矩阵在内的一小组灰度特征非常适合该任务。该方法在识别RNAscope转录本位置方面的表现与专家标注员相当，其F1分数为0.571，而专家间的F1分数为0.596。这些结果证明了灰度纹理特征在病理工作流程中自动定量RNAscope的潜力。