Growth is a widely used term in plant science and ecology, but it can have different
meanings depending on the context and the spatiotemporal scale of analysis. At 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 …

We propose the geometry-informed neural operator (GINO), a highly efficient approach for
learning the solution operator of large-scale partial differential equations with varying …

Advances in microscopy hold great promise for allowing quantitative and precise
measurement of morphological and molecular phenomena at the single-cell level in …

Accurate segmentation of objects in microscopy images remains a bottleneck for many
researchers despite the number of tools developed for this purpose. Here, we present …

We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data.
It offers easy to use manual and automated image segmentation routines that can be rapidly …

The remarkable developments in computer vision and machine learning have changed the
methodologies of many scientific disciplines. They have also created a new research field in …

Automated building processes that enable efficient in situ resource utilization can facilitate
construction in remote locations while simultaneously offering a carbon-reducing alternative …

This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of
dynamic scenes using forward war**. Existing methods often adopt a static NeRF to …

Retrieving the complex responses of individual cells in the native three-dimensional tissue
context is crucial for a complete understanding of tissue functions. Here, we present …