Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection

Among different biological studies, the analysis of leukocyte recruitment is fundamental for the comprehension of immunological diseases. The task of detecting and counting cells in these studies is, however, commonly performed by visual analysis. Although many machine learning techniques have been successfully applied to cell detection, they still rely on domain knowledge, demanding high expertise to create handcrafted features capable of describing the object of interest. In this study, we explored the idea of transfer learning by using pre-trained deep convolutional neural networks (DCNN) as feature extractors for leukocytes detection. We tested several DCNN models trained on the ImageNet dataset in six different videos of mice organs from intravital video microscopy. To evaluate our extracted image features, we used the multiple template matching technique in various scenarios. Our results showed an average increase of 5.5% in the \(\text {F}_{1}\)-score values when compared with the traditional application of template matching using only the original image information. Code is available at: https://​github.​com/​brunoggregorio/​DCNN-feature-extraction.