Nuclei segmentation using deep neural networks

Abstract

One of the most important tools incancer diagnosis, prognosis, and grading is the analysis and interpretation of stained tumor sections, which is mostly done manually by pathologists. With the advent of digital pathology, that provides us with challenging opportunity to automatically analyze huge amounts of these complex image data in order to derive biological conclusions from them in order to study cellular phenotypes on a wide scale. The automatic segmentation of cell nuclei from this type of image data is one of the bottlenecks for such techniques. Cell nucleissegmentation is essential for a variety of bioimaging tasks, including cell counting and tracking, cell morphology characterization, and molecular expression quantification. Accurate automatic nuclei segmentation is of special interest in high-throughput applications of microscopic images of cells or tissues. In the image processing world, cell nuclei segmentation is an open challenge and a hot topic of research. We used a fully automated approach for segmenting nuclei from histopathology image data using deep neural network trained on a set of manually annotated images from scratch. The dataset that we used in our work was provided by the Department of Biomedical Engineering, Case Western Reserve University, USA [48]. We built our deep neural network using a modified version of U-Net architecture. To evaluate our model, we used three different metrics which are the Pixel Accuracy (PA), Intersection over Union (IoU), and the dice-coefficient. The results obtained are as follow: 0.98 for PA, 0.57 for IoU, and 0.38 for dice-coefficient.