The blog

With your data in WEBKNOSSOS, you can create beautiful, professional-looking videos, which has become much easier now that we have AI coding agents (e.g. Claude).

Maximum Intensity Projection (MIP) renders your dataset directly in the 3D viewport. It is similar to meshes, giving you a spatial view of your data, but it applies to any image layer rather than only segmentations, and it needs no precomputation.

WEBKNOSSOS allows researchers and students to explore large 3D microscopy datasets directly in the browser. We spoke with Ahmed Elewa, assistant professor at Miami University, about how he used WEBKNOSSOS to teach cell biology through a hands-on course project.

At the European Molecular Biology Laboratory (EMBL) in Hamburg, an X-ray imaging team has established a fully automated pipeline that transfers reconstructed synchrotron X-ray tomography datasets directly into a browser-based 3D environment powered by WEBKNOSSOS.

We tested the AI features of WEBKNOSSOS on a real-world volume EM dataset of Mouse Cortex. We trained a custom instance segmentation model and deployed it across the full 140GB volume.

With these exercises, students will explore the brain in 3D at high resolution, identify cell processes, and learn to recognize synapses.

These exercises, designed by Prof. Berson. from Brown University, will help your students get familiar with connectomic analysis of retinal neurons.

The quick-select tool now works in 3D and can predict up to 16 sections! Moreover, a simple click now suffices to segment a cell - no need to draw a rectangle.

For the Connectomics conference, we wanted to create neuron sculptures to showcase the beauty and complexity of the brain through art. As many of you know, the shape and architecture of neurons are extremely challenging to reproduce.

In this interview with Prof. David Berson from Brown University, you will learn about his pioneering research on the retina, how EM-based connectomics has influenced his work, and get insights into his latest project.

Embark on a journey with two scientists in London as they navigate the challenges of managing large datasets from a serial electron microscope.

Philipp Otto has been one of the principal developers of WEBKNOSSOS for a decade. In this interview, he will share his story and insights from behind the scenes of developing such a product, including the challenges and the enjoyment it brings.

2023 was a year full of new features for WEBKNOSSOS, with exciting additions: from AI-powered quick-select tools to improved touch device support, here are the standout features we want to make sure you haven’t missed!

We are excited to introduce a new feature in WEBKNOSSOS for creating professional-looking animations of your data.

You can now access segment statistics in WEBKNOSSOS! The segments statistics include essential information like cell count, the volume of the segments, their positions, as well as their bounding boxes.

WEBKNOSSOS added support for time series datasets, opening up a new dimension in your data exploration. Now, you can not only view your data in 3D but also in 4D, adding the element of time.

Using WEBKNOSSOS on an iPad offers two key advantages: seamless data visualization on the go and intuitive annotation with the Apple Pencil. Discover in this tutorial how to show and annotate your data with an iPad.

In this tutorial, we share helpful tips on using pen tablets for efficient data annotation with WEBKNOSSOS. You will learn to program your tablet and pen buttons to match the WEBKNOSSOS shortcuts and we will show you a complete annotation workflow.

Sahil Loomba is a post-doctoral research scientist at the Max Planck Institute for Brain Research. In this interview, he will discuss the research’s origins, the encountered challenges, the tools and techniques employed, and his perspective on the future development of comparative connectomics across not only species, but also ages and disease models.

We are excited to announce the integration of Meta’s new AI model, SAM: Segment Anything, into WEBKNOSSOS! Learn how to make the best out of the AI option in WEBKNOSSOS.

In this tutorial, we will learn how to download data as a Tiff stack with the WEBKNOSSOS UI, how to automate this process, and how to incorporate an N5 remote dataset into WEBKNOSSOS.

In this tutorial, we will explore the annotation features of WEBKNOSSOS. We will cover everything from opening and annotating a published dataset to utilizing both basic and advanced annotation tools.

In this tutorial, you will discover how to efficiently manage and share your data.

This tutorial explains how to store data externally by converting data into OME-Zarr and setting up a static file server for use with WEBKNOSSOS.

The folder structure in WEBKNOSSOS allows you to have your datasets structured, organized, and easily accessible.

Follow our journey of redesigning the branding of WEBKNOSSOS to align it more closely with our identity and values.

Read all about the top features of 2022: quick-select, interpolation tool, collaborative annotations, and more.

Array tomography and Serial Block-face Electron Microscopy are two microscopy techniques that work in a similar way and are hard to understand. We playfully explained and compared these techniques using a fig (AT) and a tomato (SBEM).

How would you explain SBEM/volume EM and 3D reconstruction to your grandparents? With a camera, a knife, and a kiwi fruit.

Today, we would like to highlight some features and quality of life improvements WEBKNOSSOS acquired over the summer. They will accelerate your workflows and make the annotating of EM data easier.

This tutorial explains how to load any Zarr-format-based datasets into WEBKNOSSOS. We support reading Zarr dataset from the local file system, cloud providers such as S3 and Google Cloud, as well as through any web server over HTTP.

In this blog post, we will share some progress on streaming any dataset from WEBKNOSSOS to compatible third-party programs through the Zarr interface.

Today, we would like to highlight some features and quality of life improvements WEBKNOSSOS acquired over the summer. They will accelerate your workflows and make the annotating of EM data easier.

Carles Bosch, principal laboratory research scientist at the Sensory Circuits and Neurotechnology Laboratory of the Francis Crick Institute, explains his research on glomerular column circuits in the mouse olfactory bulb.

Daniel Werner, data scientist at scalable minds, tells us about the analysis and reconstruction of 9 vEM datasets he contributed to for a comparative connectomic study among species, by the Max Planck Institute for Brain Research.

In this tutorial, we will tell you about the interpolation feature. Draw on one slice, move forward by a few slices, draw again… and WEBKNOSSOS will interpolate the segments between the two annotated slices.

Today, we will have a chat with one of our colleagues: Valentin Pinkau. As an experienced data scientist specializing in connectomics, he has some interesting insights to share.

With the recent publication of the “Dense connectomic reconstruction in layer 4 of the somatosensory cortex“ paper by the Max-Planck-Institute for Brain Research, we showcase WEBKNOSSOS and the ML reconstruction pipeline Voxelytics.

Here, we share some tips on how to reconstruct biological objects (e.g. neurons) from tera- or peta-scale image datasets.