  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 grey
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 grey level features, including Grey Level Dependence
Matrix and Neighbouring Grey 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 grey level texture features for automated quantification of
RNAscope in the pathology workflow.
