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 …

Generating numbers has become an almost inevitable task associated with studies of the
morphology of the nervous system. Numbers serve a desire for clarity and objectivity in the …

Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and
clinical analysis, enabling the study of cellular expression patterns and cell lineages …

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade
neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the …

Background High-content screening (HCS) is a pre-clinical approach for the assessment of
drug efficacy. On modern platforms, it involves fluorescent image capture using three …

Gesture recognition utilizes deep learning network model to automatically extract deep
features of data; however, traditional machine learning algorithms rely on manual feature …

The Brain-Derived Neurotrophic Factor is one of the most important trophic proteins in the
brain. The role of this growth factor in neuronal plasticity, in health and disease, has been …

Nuclear speckles, also known as interchromatin granule clusters (IGCs), are subnuclear
domains highly enriched in proteins involved in transcription and mRNA metabolism and …

Abstract (250max) Background Analyzing images to accurately estimate the number of
different cell types in the brain using automatic methods is a major objective in …

In this article, we propose a hierarchical deep learning framework for the nuclei 3D
reconstruction from a stack of microscopic images representing 3D cancer cell culture. The …