Abstract: In recent years, deep learning has been widely utilized in the fields of biomedical image segmentation and cellular image analysis. Supervised deep neural networks trained on annotated data ...

This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual information ...

Single-cell RNA transcriptomics allows researchers to broadly profile the gene expression of individual cells in a particular tissue. This technique has allowed researchers to identify new subsets of ...

Abstract: State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from background ...

Spatial transcriptomics is undergoing rapid advancements and iterations. It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells.

Cell segmentation is a crucial step in numerous biomedical imaging endeavors—so much so that the community is flooded with publicly available, state-of-the-art segmentation techniques ready for out-of ...

Identifying and delineating cell structures in microscopy images is crucial for understanding the complex processes of life. This task is called "segmentation" and it enables a range of applications, ...