Automated nuclei segmentation techniques in histopathological image analysis continue to
improve. The machine learning model requires the annotation of large data sets which is a …

Accurate segmentation of nuclei is an essential step in analysis of digital histology images
for diagnostic and prognostic applications. Despite recent advances in automated …

This paper presents a novel computerized technique for the segmentation of nuclei in
hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to …

Nuclei segmentation is a fundamental upstream task of digital pathology image analysis.
Existing nuclei segmentation methods usually require pixel-level labeled images from …

In the medical field, hematoxylin and eosin (H&E)-stained histopathology images of cell
nuclei analysis represent an important measure for cancer diagnosis. The most valuable …

Automated nuclear segmentation on histopathological images is a prerequisite for a
computer-aided diagnosis system. It becomes a challenging problem due to the nucleus …

Introduction Automatic nuclear segmentation in digital microscopic tissue images can aid
pathologists to extract high-quality features for nuclear morphometrics and other analyses …

The foundational task of pathological diagnosis is to accurately segment the nuclei in
pathology images. Deep learning has become a well-validated method of nuclei …

Segmentation and classification of various types of nuclei in tumor tissue histology images is
a crucial step in development of computer aided diagnostic systems. Existing techniques for …

Background Since nuclei segmentation in histopathology images can provide key
information for identifying the presence or stage of a disease, the images need to be …