Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning.

ABSTRACT: Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. However the images are of a large size with nerves having substantial variation in morphology that renders accurate and objective quantification difficult using existing manual and automated counting techniques. Manual counting is precise, but time-consuming, susceptible to inconsistency and has a high rate of false negatives. Existing automated techniques using digitised tissue sections and colour filters are sensitive, however, have a high rate of false positives. In this paper we develop a new automated nerve detection approach, based on a deep le

SUBMITTER: Astono IP 

PROVIDER: S-EPMC8912900 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature