ABSTRACT
The manipulation and stimulation of cell growth is invaluable for neuroscience research such as brain-machine interfaces or applications of neural tissue engineering. For the implementation of such research avenues, in particular the analysis of cells' migration behaviour, and accordingly, the determination of cell positions on microscope images is essential, causing a current need for labour-intensive, manual annotation efforts of the cell positions. In an attempt towards automation of the required annotation efforts, we i) introduce NeuroCellCentreDB, a novel dataset of neuron-like cells on microscope images with annotated cell centres, ii) evaluate a common (bounding box-based) object detector, faster region-based convolutional neural network (FRCNN), for the task at hand, and iii) design and test a fully convolutional neural network, with the specific goal of cell centre detection. We achieve an F1 score of up to 0.766 on the test data with a tolerance radius of 16 pixels. Our code and dataset are publicly available.
