… analysis software already incorporates machine learning algorithms to assist researchers
and clinicians in quantifying and segmenting histopathological images. These tools have …

… The segmentation of nuclei in cancer histopathology images can be … -stage segmentation
method to obtain cellular structures in high-dimensional histopathological images of renal cell …

… 2.1 Need of nucleus segmentation Segmenting cell nuclei in histopathology images is the
preliminary step in analyzing current imaging data for biological and biomedical purposes. …

… of cells for an efficient segmentation. … for histopathological images. Then, the k-means
clustering algorithm is basically applied to segment cellular and non-cellular structures in an image…

… CONCLUSION This paper proposed a deep learning based approach for cell nuclei semantic
segmentation in histopathology images of breast cancer. The network structure consists of …

… quantification, and cell recognition. The appearance and distribution of cells in histopathological
images are quite different from the target objects in natural images; therefore, existing …

… Manual segmentation … histopathological datasets report poor performance on other datasets
due again to the high variability in acquisition parameters and biological properties of cells …

… Therefore, DL-based approaches are now a gold standard for different colon cancer
related applications namely gland segmentation, tumour micro-environment and cells …

… nuclei segmentation framework for histopathology images using … (2) weakly supervised nuclei
segmentation. The goal of the … -throughput histopathological image analysis via robust cell …

… segmentation in histopathology images plays a key role in digital pathology. It is considered
a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell … cell …