Cell nuclei segmentation in histopathology images based on deepneural networks

Abstract

Despite the significant progress in understanding cancer’s biological basis, it continues to confound patients, researchers, and physicians as a self-sustaining and adaptive disease that interacts dynamically with its environment. Analysis of stained tumor sections has a staggering importance in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. Because most of the human body's billions of cells have a nucleus full of DNA, the genetic code that programs each cell, most analyses begin with identifying the cell's nuclei. Researchers can better grasp the underlying process by identifying the nuclei. They can measure how different samples react to a certain drug. However, huge volumes of medical images make manual analysis challenging, time consuming, and a tedious task. Therefore, other techniques are necessary to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes at a large scale. The automatic segmentation of cell nuclei from this type of image data is one of the bottlenecks for such techniques. We present a fully automated workflow to segment nuclei from histopathology images by using deep neural networks trained from a set of semi automatically annotated image data. In our work, we have used the PSB 2015 crowd-sourced nuclei dataset. We have built three models using three different architectures: U-Net, U-Net++, and a modified version of U-Net architecture.