# Nuclei segmentation Nuclei segmentation in 3D data is challenging because of background intensity, uneven intensity in Z-dimension, noise and simply the amoung of pixels which need to be processed. Real-time experience while configuring a workflow for nuclei segmentation can be achieved when utilizing classical methods such as filtering, thresholding and watershed techniques. It is recommended to utilize modern [GDDR6-based GPU hardware](https://clij.github.io/assistant/installation#hardware) for 3D segmentation. ## How to do 3D cell nuclei segmentation Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow such a workflows: * Your dataset * CLIJx-Assistant Starting point * [Optional: Noise removal and Background subtraction] * Threshold DoG * Parametric Watershed * Connected Components Labeling * Maximum Z projection After assembling your workflow, put these operations next to each other, change the parameters. <iframe src="images/incubator_segmentation_3d_nuclei.mp4" width="540" height="540"></iframe> [Download video](images/incubator_segmentation_3d_nuclei.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] There are many ways for detecting nuclei and extending their size, e.g. to study neighborhood relationships. <iframe src="images/clijxa_teaser1_fast.mp4" width="800" height="640"></iframe> [Download video](images/clijxa_teaser1.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] Back to [CLIJx-Assistant](https://clij.github.io/assistant) [Imprint](https://clij.github.io/imprint)