Hi! My name is Mara. I am studying computational modelling and simulation in life sciences and am working as a student assistant at ScaDS.AI in Leipzig in the group of Dr. Robert Haase. I would like to facilitate and increase the exchange between computer scientists and biologists. Hereby, I focus on bio-image analysis applications. Happy reading and coding!
Scientific field: Computational biology
Microscopy background: Image Analysis
Posted by Mara Lampert, on 18 July 2024
Prompt engineering and its importance Communication is key. This saying is not only true in day-to-day life, but also when interacting with Generative Artificial Intelligence, a system able to generate text, images or other output types in response to prompts. In prompt engineering, we use natural language to describe the task that a Large LanguagePosted 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 orPosted 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.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,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,