Single nucleus segmentation is a frequent challenge of microscopy image processing, since
it is the first step of many quantitative data analysis pipelines. The quality of tracking single …

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 …

In computational pathology, automatic nuclei instance segmentation plays an essential role
in whole slide image analysis. While many computerized approaches have been proposed …

The detection and segmentation of individual cells or nuclei is often involved in image
analysis across a variety of biology and biomedical applications as an indispensable …

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

The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …

The development of deep learning approaches to detect, segment or classify structures of
interest has transformed the field of quantitative microscopy. High-throughput quantitative …

Diagnostic, prognostic and therapeutic decision-making of cancer in pathology clinics can
now be carried out based on analysis of multi-gigapixel tissue images, also known as whole …

Simple Summary Bone marrow metastasis frequently occurs in patients with solid cancers
and most often leads to poor outcome. Yet, the composition of bone marrow metastases …

Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …