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How to write a bug report

Posted by Mara Lampert, on 3 April 2024

Imagine you try out a software tool for the analysis of your data and for some reason it is not working in the way you expected. Neither the user documentation, nor forums like image.sc or stack overflow can provide answers. Then it is likely that you either found a bug in the software tool or

“Image analysis workshop” in Dublin (Ireland) - 24-25/04/2024

Posted by Stefania Marcotti, on 14 March 2024

This two-day workshop on April 24th-25th 2024 is designed to empower microscopists and researchers with the necessary skills and tools to confidently analyse their data. Under the instruction of experienced core facility staff from the Francis Crick Institute and King’s College London, participants will be introduced to open-source tools such as FIJI and napari, and

Postdoctoral Research Associate - SMLM data assessment at King's College London

Posted by siân culley, on 8 September 2023

Closing date: 2nd October 2023. Full time post offered on a fixed-term contract until 31st August 2026. Salary range: Grade 6, £42,405 – £47,178 per annum, including London Weighting Allowance. Link to apply: https://www.kcl.ac.uk/jobs/074054-research-associate-department-of-randall-cell-and-molecular-biophysics Informal enquiries to susan(dot)cox(at)kcl.ac.uk or sian(dot)culley(at)kcl.ac.uk Job Description Applications are invited for a postdoctoral research position to work on data assessment

Tracking in napari

Posted by Mara Lampert, on 1 June 2023

In this blog post we are exploring cell tracking in napari. Two important processes in normal tissue development and disease are cell migration and proliferation. To gain a better understanding on these processes, tracking in time-lapse datasets is needed. By measuring track properties, like velocity and the total travelled distance, spatio-temporal relationships can be studied.

Feature extraction in napari

Posted by Mara Lampert, on 3 May 2023

This blog post revolves around extracting and selecting features from segmented images. We will define the terms feature extraction and selection. Also, we will learn how to categorize features and can look up specific features in a glossary. Furthermore, we will explore how to extract features in napari. Definition of feature extraction During feature extraction,

Quality assurance of segmentation results

Posted by Mara Lampert, on 13 April 2023

This blog post revolves around determining and improving the quality of segmentation results. A common problem is that this step is often omitted and done rather by the appearance of the image segmentation than by actually quantifying it. This blogpost aims to show different ways to achieve this quantification as this leads to reproducibility. Therefore,

Annotating 3D images in napari

Posted by Mara Lampert, on 30 March 2023

This blog post revolves around generating ground truth in 3D images for segmentation. Therefore, we will define what ground truth is and how we can generate it using napari in a time-efficient way. We will also learn about difficulties of annotating alone or in groups and address possible solutions for both. Challenges of image segmentation

Rescaling images and pixel (an)isotropy

Posted by Mara Lampert, on 2 March 2023

This blog post shows the importance of rescaling 3D image data and what anisotropy of pixels/voxels has to do with it. We will see which different options exist to rescale an image as well as their advantages and limitations. We will also apply a commonly used segmentation algorithm, Voronoi-Otsu-Labeling, on the original and the rescaled

VollSeg and its extension Napari plugins

Posted by varun kapoor, on 15 January 2023

In this article we present the Napari plugin VollSeg and its ongoing extension plugins that we are creating as a part of the CZI grant awarded successively to the non-profit company KapoorLabs, Paris. VollSeg is a Napari plugin for performing semantic, instance segmentation and denoising using the seed pooling approach that requires StarDist and either

TrackMate-Oneat: Auto Track correction using deep learning networks

Posted by varun kapoor, on 4 July 2022

During the tracking of motile cells, solving the problem of linking objects between two consecutive timepoints becomes even more complicated, if the cells divide or undergo cell death. In the terms of trajectories, this means the addition of trajectory branches and terminations. However, dividing and dying cells are characteristic in their shape, and leveraging this

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Microscopy-related articles from our journals

HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples Development 2024 151: dev202307
filoVision – using deep learning and tip markers to automate filopodia analysis J Cell Sci 2024 137: jcs261274
C omputational tools for quantifying actin filament numbers, lengths, and bundling Biology Open 2024 13: bio.060267

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