Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry.
Despite substantial progress, there is a need to improve accuracy, speed, level of …

Automated cell nucleus segmentation is the key to gain further insight into cell features and
functionality which support computer-aided pathology in early diagnosis of diseases such as …

Automated image analysis of histopathology specimens could potentially provide support for
early detection and improved characterization of breast cancer. Automated segmentation of …

Automatic quantification of cell nuclei in immunostained images is highly desired by
pathologists in diagnosis. In this paper, we present a new approach for the segmentation of …

Accurate nuclei detection and segmentation in histological images is essential for many
clinical purposes. While manual annotations are time-consuming and operator-dependent …

Automatic segmentation of cell nuclei is critical in several high‐throughput cytometry
applications whereas manual segmentation is laborious and irreproducible. One such …

Fully-automated nuclear image segmentation is the prerequisite to ensure statistically
significant, quantitative analyses of tissue preparations, applied in digital pathology or …

Image segmentation is an essential step in many image analysis pipelines and many
algorithms have been proposed to solve this problem. However, they are often evaluated …

We present a method to separate clustered nuclei from fluorescence microscopy cellular
images, using shape markers and marking function in a watershed-like algorithm. Shape …

This paper addresses the task of nuclei segmentation in high-resolution histopathology
images. We propose an automatic end-to-end deep neural network algorithm for …