Abstract:
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 can be time and resource consuming.
Due to cells touching each other, a non-uniform background, shape and size variations
of cells, and different techniques of image acquisition, the task becomes even more
difficult. In this paper we describe a convolutional neural network approach, using a
Faster-RCNN architecture later also combined with a U-Net neural network, for cell
counting and possibly segmentation in a raw microscopic picture.
