In this paper, we review the integration of microfluidic chips and computer vision, which has
great potential to advance research in the life sciences and biology, particularly in the …

The instance segmentation of cell images is the basis for conducting cell research and is of
great importance for the study and diagnosis of pathologies. To analyze current situations …

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

Automated nuclear segmentation in histopathological images is a prerequisite for a
computer-aided diagnosis framework. However, it remains a challenging problem due to the …

Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei
segmentation presents many challenges because they usually create complex clusters in …

One of the most popular methods in the diagnosis of breast cancer is fine-needle biopsy
without aspiration. Cell nuclei are the most important elements of cancer diagnostics based …

Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic
effects for treatment of trauma and chronic diseases. The proliferative potential …

For the analysis of medical images, one of the most basic methods is to diagnose diseases
by examining blood smears through a microscope to check the morphology, number, and …

This paper presents a new deep regression model, which we call DeepDistance, for cell
detection in images acquired with inverted microscopy. This model considers cell detection …

The brine shrimp Artemia is a widely used cost-effective diet in aquaculture. In many Artemia
studies, eg, in a quality assessment of Artemia hatching, an automated method for detecting …