TimScherr/KIT-GE-3-Cell-Segmentation-for-CTC
Distance-transform-prediction-based segmentation method used for our submission to the 6th edition of the ISBI Cell Tracking Challenge 2021 as team KIT-Sch-GE (2) (now KIT-GE (3)).
This project provides an automated way to accurately outline and identify individual cells within microscopy images, a process known as cell segmentation. You input raw microscopy image data, and it outputs precise boundaries for each cell detected. This tool is designed for biologists, researchers, and anyone working with cell culture imaging who needs to quantify or analyze individual cells.
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Use this if you need to precisely segment and analyze various cell types in microscopy images, especially for tasks related to the Cell Tracking Challenge.
Not ideal if you lack a CUDA-capable GPU, have less than 32GB RAM, or require a simple point-and-click graphical interface without command-line interaction.
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MIT
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Last pushed
Dec 14, 2022
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