An Automatic Cell Nuclei Segmentation based on Deep Learning Strategies

Abstract

Automatic analysis of histopathology specimens images can be utilized in early extraction and detection of diseases such brain tumor, breast malignancy, colon cancer etc. The early detection of cancer may allow patients to take proper treatment. In this paper, an automatic cell nuclei segmentation based on deep learning strategies using 2-$D$ histological images is proposed. In the proposed approach U-Net architecture is used and its hyper parameters are tuned to segment the cell nuclei. The proposed solution is built upon the highly adaptive nature of U - Net architecture. The task of nuclei segmentation in the proposed approach includes detection of nuclei in an image and extracting the foreground, while segmenting the connected foreground area into separated nuclei masks. In the experimental results the proposed approach is tested using the dataset having histopathological cell images of breast cancer. The results shows that the proposed deep learning based approach achieved the 86 % average accuracy in segmentation of cell nuclei and also outperforms the other deep learning architectures.