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 …

Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the
uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. …

Deep learning is transforming the analysis of biological images, but applying these models
to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native …

Live-cell imaging experiments have opened an exciting window into the behavior of living
systems. While these experiments can produce rich data, the computational analysis of these …

This is the first comprehensive study of per- and polyfluoroalkyl substances (PFAS) in a
coastal food web of the US North Atlantic, in which we characterize the presence and …

Concurrent advances in imaging technologies and deep learning have transformed the nature
and scale of data that can now be collected with imaging. Here we discuss the progress …

Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the
uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels that …

Deep learning is transforming the ability of life scientists to extract information from images.
These techniques have better accuracy than conventional approaches and enable …

The effects of nonpoint source nutrients on estuaries can be difficult to pinpoint, with researchers
often using indicator species, monitoring, and models to detect influence and change. …

Cells are the fundamental unit of biological organization, and identifying them in imaging
data-cell segmentation-is a critical task for various cellular imaging experiments. While deep …