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 for 3D segmentation.
Open your data set. Start the CLIJx-Assistant 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
- Connected Components Labeling
- Parametric Watershed
- Threshold DoG
- [Optional: Noise removal and Background subtraction]
- CLIJx-Assistant Starting point
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