State-of-the-art light and electron microscopes are capable of acquiring large image
datasets, but quantitatively evaluating the data often involves manually annotating structures�…

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�…

We present DeepMIB, a new software package that is capable of training convolutional
neural networks for segmentation of multidimensional microscopy datasets on any�…

The development of deep learning approaches to detect, segment or classify structures of
interest has transformed the field of quantitative microscopy. High-throughput quantitative�…

We introduce YeastSpotter, a web application for the segmentation of yeast microscopy
images into single cells. YeastSpotter is user-friendly and generalizable, reducing the�…

Microscopy images are rich in information about the dynamic relationships among biological
structures. However, extracting this complex information can be challenging, especially�…

With recent advances in high-throughput, automated microscopy, there has been an
increased demand for effective computational strategies to analyze large-scale, image�…

High-resolution microscopy images of tissue specimens provide detailed information about
the morphology of normal and diseased tissue. Image analysis of tissue morphology can�…

The identification of cell borders ('segmentation') in microscopy images constitutes a
bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae�…

Automated analysis of multi-dimensional microscopy images has become an integral part of
modern research in life science. Most available algorithms that provide sufficient�…