Automated detection of macrophages in quantitative phase images by deep learning using a mask region-based convolutional neural network

We explored a Mask Region-based Convolutional Neural Network (Mask R-CNN) to detect macrophages in quantitative phase images, which were acquired by digital holographic microscopy (DHM), an interferometry-based variant of quantitative phase imaging (QPI). The Mask R-CNN deep learning architecture is capable to detect and segment single macrophage cells in quantitative phase images and allows to perform both tasks in a multi-stage process. Our results show that the combined detection and segmentation of cells through Mask R-CNN-based automated evaluation prospects a fast and robust screening in label-free high throughput microscopy.