Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …

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 …

Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …

Object segmentation is an important step in the workflow of computational pathology. Deep
learning based models generally require large amount of labeled data for precise and …

Nuclei segmentation is a vital step for pathological cancer research. It is still an open
problem due to some difficulties, such as color inconsistency introduced by non-uniform …

Nuclei segmentation and classification of hematoxylin and eosin-stained histology images is
a challenging task due to a variety of issues, such as color inconsistency that results from the …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …

Counting of mitotic figures is a fundamental step in grading and prognostication of several
cancers. However, manual mitosis counting is tedious and time-consuming. In addition …

Nucleus segmentation is a challenging task due to the crowded distribution and blurry
boundaries of nuclei. Recent approaches represent nuclei by means of polygons to …

Nuclear segmentation of histopathological images is a crucial step in computer-aided image
analysis. There are complex, diverse, dense, and even overlapping nuclei in these …