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

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. Author summary: The study of nerves as a prognostic marker for cancer is becoming increasingly important. However, accurate quantification of nerves in cancer specimens is difficult to achieve due to limitations in the existing manual and automated quantification methods. Manual quantification is time-consuming and subject to bias, whilst automated quantification, in general, has a high rate of false detections that makes it somewhat unreliable. In this paper, we propose an automated nerve quantification approach based on a novel deep learning model structure for objective nerve quantification in immunohistochemistry specimens of thyroid cancer. We evaluate the performance of the proposed approach by comparing it with existing manual and automated quantification methods. We show that our proposed approach is superior to the existing manual and automated quantification methods. The proposed approach is shown to have a high precision as well as being able to detect a significant number of nerves not detected by the experts in manual counting. [ABSTRACT FROM AUTHOR]