Improvements in lymphocytes detection using deep learning with a preprocessing stage

Lymphocytes are a type of white blood cell that are part of the adaptive immune system and respond to infectious microorganisms. Due to this key role, its detection and quantification allow analyzing the overall status of the immune system. However, the manual detection of lymphocytes in tissue slices is a laborious task, and it depends on the expertise of the observer, reason why an automated image analysis helps to speedup this process. Several different techniques have been used to automatize this task, such as morphological operations, classification algorithms, and, more recently, deep learning approaches. In this work, we propose two preprocessing methods for improving the lymphocytes detection in digital images. Furthermore, this study proposes a change in the ground truth, in order to turn it into a segmentation map, and evaluate semantic segmentation models in a dataset that originally does not allow this approach. Two deep learning models (Segnet and U-Net) with different backbones (VGG16 and Resnet50) were used for the training and test sets. One of the proposed methods showed an F1-score 11% higher than simply using a color normalization. The results were compared with other state-of-the-art studies, showing one of the best-ranked results.