  Digital pathology is one of the most significant developments in modern
medicine. Pathological examinations are the gold standard of medical protocols
and play a fundamental role in diagnosis. Recently, with the advent of digital
scanners, tissue histopathology slides can now be digitized and stored as
digital images. As a result, digitized histopathological tissues can be used in
computer-aided image analysis programs and machine learning techniques.
Detection and segmentation of nuclei are some of the essential steps in the
diagnosis of cancers. Recently, deep learning has been used for nuclei
segmentation. However, one of the problems in deep learning methods for nuclei
segmentation is the lack of information from out of the patches. This paper
proposes a deep learning-based approach for nuclei segmentation, which
addresses the problem of misprediction in patch border areas. We use both local
and global patches to predict the final segmentation map. Experimental results
on the Multi-organ histopathology dataset demonstrate that our method
outperforms the baseline nuclei segmentation and popular segmentation models.
