Unsupervised Deep Learning Method for Cell Segmentation of Confocal Microscopy Images

The use of image segmentation is widespread in many different fields of research including medicine, biology, computer vision and others. In most cases, image segmentation is done through the use of supervised learning approaches, which utilize ground truth segmentation masks. However, in biomedical research, segmentation masks are often not available, which leads to the use of unsupervised segmentation approaches. In these situations, filtering paired with region expansion and reduction and edge detection techniques are used, which in some cases require time-consuming manual parameter tuning, in order to achieve satisfactory results. This paper implements fully unsupervised image segmentation based on a deep learning convolutional neural network to segment cell geometries from confocal microscopy images of an in vitro model of angiogenesis. The proposed network architecture is a W-Net which contains a single U-Encoder and U-Decoder, both of which contain 3 convolution blocks and 3 deconvolution blocks, with 2 convolution layers and one max-pooling or up-convolution layer respectively. The results were evaluated using available ground truth images and achieved a pixel wise classification accuracy score of 98.73% and 73.71% intersection over union. The achieved high degree of accuracy shows great promise in cell segmentation without a need for ground truth masks over time and increases the accuracy of calculations of exerted forces.