Fig 2. Overview of the tracking software.

Using ground-truth data of nucleus locations in microscopy images (obtained from manual detection) a convolutional neural network is trained. This trained network is then used to detect (step 1) cells in new images. The detections are then linked over time (step 2), after which the user manually corrects the output with the help of an error checker (step 3). In principle, the manually corrected data can be used as additional ground-truth data to improve the training of the convolutional neural network, leading to an efficient, iterative improvement in performance of the convolutional neural network (dashed line).