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

PLoS Comput Biol. 2022 Feb 28;18(2):e1009912. doi: 10.1371/journal.pcbi.1009912. eCollection 2022 Feb.


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 learning model with an augmented classification structure. This approach involves pre-processing to extract the image patches for the deep learning model, followed by pixel-level nerve detection utilising the proposed deep learning model. Outcomes assessed were a) sensitivity of the model in detecting manually identified nerves (expert annotations), and b) the precision of additional model-detected nerves. The proposed deep learning model based approach results in a sensitivity of 89% and a precision of 75%. The code and pre-trained model are publicly available at https://github.com/IA92/Automated_Nerves_Quantification.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Humans
  • Immunohistochemistry
  • Thyroid Neoplasms*

Grant support

I.P.A. has been awarded a 2016 University of Newcastle Australia (https://www.newcastle.edu.au/) Scholarship provided by UNIPRS and UNRSC 50:50. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.