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

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify
the entire complement of proteins in cells or tissues. Here, we review challenges and recent …

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 …

Background Imaging data contains a substantial amount of information which can be difficult
to evaluate by eye. With the expansion of high throughput microscopy methodologies …

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 …

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of
coronavirus disease 2019 (COVID-19), uses the viral spike (S) protein for host cell …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial
proteomics, a key challenge remains connecting images with single-cell-resolution protein …

Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …