FASTER R-CNN for cell counting in low contrast microscopic images

Cell imaging is a difficult problem in medical imaging and the acquisition of unstained frames to reduce the side effects makes this even more challenging. As the field of automation is moving forward at ever-faster rates, cell counting and classification is an omnipresent yet repetitive task that would benefit greatly from this field. The counting of contiguous cells in a specific area could provide crucial contribution to work done in clinical trials. Cell counting, sadly, is most often conducted manually by humans and is time and resource consuming. Due to cells touching each other, non-uniform background, variations in the shape and size of cells, and different techniques of image acquisition, the task becomes even more difficult. In this paper we describe and test a convolutional neural network approach, employing CNN with region proposal called Faster-RCNN for cell counting in low contrast microscopic images. Several hyperparameters of the approach are tested and preprocessing procedures are employed to achieve an average precision of 79%.