In organelle imaging, segmentation aims to accurately delineate pixels or voxels corresponding to target organelles from background, noise, and other cellular structures in microscopy images, thereby ...

Abstract: Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation ...

Chang leads assay and applications development at Takara Bio, driving the commercialization of novel spatial genomics products. She has extensive experience in single-cell multiomics technologies and ...

This project implements a complete deep learning pipeline for counting cells in microscopy images using semantic segmentation. Given fluorescence microscopy images, the model predicts a binary ...

What should I do to properly use my cell segmentation mask? Another thing I noticed is that although each cell was manually drawn and labeled in Napari, and the .tif file was converted to a binary ...

CD7 is an attractive target for chimeric antigen receptor (CAR) T-cell therapy in relapsed or refractory T-cell acute lymphoblastic leukemia (ALL). Supportive results of first-in-human studies of base ...

Aging taps us on the shoulder in many ways: wrinkles, thinning hair, loss of flexibility, slowing of the brain. But the process also unfolds at a more fundamental, microscopic level, as the energy ...

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 ...

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

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 ...