Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over
the past decade, particularly in developmental biology, cancer, immunology, and …

The tumor microenvironment (TME) is composed of many different cellular and acellular
components that together drive tumor growth, invasion, metastasis, and response to …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in
immune surveillance, maintains critical symbiotic relationships with microbiota and affects …

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and
tumors, depends on the spatial organization of their cells. In the past decade, high …

Understanding intratumoral heterogeneity—the molecular variation among cells within a
tumor—promises to address outstanding questions in cancer biology and improve the …

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …

Deep Learning (DL) methods are powerful analytical tools for microscopy and can
outperform conventional image processing pipelines. Despite the enthusiasm and …

Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in
infected tissues, the architecture and composition of which are thought to affect disease …

To define the cellular composition and architecture of cutaneous squamous cell carcinoma
(cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and …