NucMM dataset: 3d neuronal nuclei instance segmentation
The goal is to segment and identify automatically each cell nuclei in EM images. To solve such task pairs of EM images and their corresponding instance segmentation labels are provided. Below a pair example is depicted:
In this dataset
3D images of size
(64, 64, 64) voxels, for
(z,x,y) axes, are used for train while the test is
done over the whole Zebrafish volume. Here is a training sample and its ground truth:
You need to download NucMM dataset first from these link. Once you have donwloaded this data you need to create a directory tree as described in Data preparation. To adapt the
.h5 file format provided by MitoEM authors into
.tif files you can use the script h5_to_tif.py.
To create the YAML file you can use the template 3d_instance_segmentation.yaml which is prepared for this tutorial.
To run it via command line or Docker you can follow the same steps as decribed in Run.
The results follow same structure as explained in Results. The results should be something like the following:
The resulting instance segmentation should be something like this: