A Semi-Supervised Approach to Cell Nuclei Segmentation

Abstract

Medical image analysis is one of the most important applications in the fields of computer vision and medicine. Developing specific models that extract information from biomedical images could help doctors provide faster and more accurate diagnosis. One highly significant problem in this domain is called cell nuclei segmentation and it consists in identifying if a pixel from a medical image is a part of cell nucleus or not. Building such a model would help specialists detect several biomarkers of tumors. Nowadays, it is a well-known fact that the best tools for computer vision tasks are using deep neural networks, but they have one vulnerability - the data. Accurate and robust models rely on numerous labeled samples, but obtaining such a large, various annotated data set for cell nuclei segmentation is very difficult. One solution to tackle this problem is to use semisupervised learning which extends the data set with unlabeled samples during training. The aim of this paper is to build a model that performs cell nuclei segmentation in a semi-supervised manner by using Cross Pseudo-Supervision. We have run our experiments on 2018 Data Science Bowl dataset and we have achieved an IoU of 0.9077 using a fully-supervised setting, an IoU of 0.8493 using the same architecture, but in a semi-supervised setting, and an IoU of 0.7734 with a U-Net trained only on the labeled samples fed to the semi-supervised model. These values indicate the potential of machine learning, especially in those cases when only a small amount of labeled data are available.