A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images

Automatic detection of lymphocytes could contribute to develop objective measures of the infiltration grade of tumors, which can be used by pathologists for improving the decision making and treatment planning processes. In this article, a simple framework to automatically detect lymphocytes on lung cancer images is presented. This approach starts by automatically segmenting nuclei using a watershed-based approach. Nuclei shape, texture, and color features are then used to classify each candidate nucleus as either lymphocyte or non-lymphocyte by a trained SVM classifier. Validation was carried out using a dataset containing 3420 annotated structures (lymphocytes and non-lymphocytes) from 13 1000 × 1000 fields of view extracted from lung cancer whole slide images. A Deep Learning model was trained as a baseline. Results show an F-score 30% higher with the presented framework than with the Deep Learning approach. The presented strategy is, in addition, more flexible, requires less computational power, and requires much lower training times.