BEM-RCNN Segmentation Based on the Inadequately Labeled Moving Mesenchymal Stem Cells

This paper addresses the challenging task of moving mesenchymal stem cell segmentation in digital time-lapse microscopy sequences. A convolutional neural network (CNN) based pipeline is developed to segment cells automatically. To accommodate the data in its unique nature, an efficient binarization enhancement policy is proposed to increase the tracing performance. Furthermore, to work with datasets with inadequate and inaccurate ground truth, a compensation algorithm is developed to enrich the annotation automatically, and thus ensure the training quality of the model. Experiments show that our model surpassed the state-of-the-art. Result of our model measured by SEG score is 0.818.