Digital pathology and microscopy image analysis is widely used for comprehensive studies
of cell morphology or tissue structure. Manual assessment is labor intensive and prone to …

This paper presents an overview of methods that have been proposed for the analysis of
breast cancer histopathology images. This research area has become particularly relevant …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to
develop and validate visual biomarkers for new digital pathology datasets. We summarize …

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-
quality features for nuclear morphometrics and other analysis in computational pathology …

Detecting various types of cells in and around the tumor matrix holds a special significance
in characterizing the tumor micro-environment for cancer prognostication and research …

Nuclei mymargin segmentation is a fundamental task for various computational pathology
applications including nuclei morphology analysis, cell type classification, and cancer …

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …

Computer-aided image analysis of histopathology specimens could potentially provide
support for early detection and improved characterization of diseases such as brain tumor …

Object segmentation and structure localization are important steps in automated image
analysis pipelines for microscopy images. We present a convolution neural network (CNN) …