Simple python utility for exporting single or multiple labels from a segmentation (h5 file only) into a mesh (ply only).

• python 3.7
• numpy
• scikit-image
• h5py
• vtk
• psutil

If you are using anaconda python:

conda install -c conda-forge scikit-image h5py numpy vtk netcdf4 psutil

• 0.4 - Multiprocessing

  • Use the --multiprocessing to set the number of parallel process.
  • Revert to the skimage marching_cubes implementation insteads of ilastik.
  • Add flag --step-size to the marching_cubes algorithm, larger steps yield a coarser but faster result.
  • Generic improvement in performance.
  • Safe error handling. Now in case of a corrupted label the script does not stop.

• 0.3 - Code revamp for performances

  • use ilastik marching_cubes implementation insteads of skimage, speed gain: ~2x.
  • finding the largest connected component is now ~2x faster.
  • streamlined of decimation and smoothing that are now directly applied to the mesh (no more saving to disk in between).
  • use of vtk for exporting PLY files.
  • labels below a --min-volume thhreshold are not processed
  • performances gain: ~90% faster than v0.2:

# labels_2_ply (v0.2)
python labels_to_ply.py --path test_files/fused_dc_cropped--C00--T00020_crop_x40-1630_y520-970_predictions.h5 --dataset "merged_50000" --labels 42 69 3 88 67 --simple-name "test" --reduction 0.9
(...)
Finished!
5 objects in 0:01:46.949603 [HH:mm:ss]

# seg2mesh (v0.3)
python seg2mesh.py --path test_files/fused_dc_cropped--C00--T00020_crop_x40-1630_y520-970_predictions.h5 --dataset "merged_50000" --labels 42 69 3 88 67 --out-name "test" --reduction 0.9
(...)
Finished!
5 objects in 0:01:07.503175 [HH:mm:ss]

• 0.2 - Flexible output and batch mode

  • path and name of the output file can be specified
  • getting all the labels in a file with --all
  • implemented batch modes:
    • batch: specific labels from specific files
    • batch-all: all labels from all files

• 0.1 - Initial release

To extract specific labels from a file, generating a .ply file for each label:

python seg2mesh.py --path *path to segmentation file*.h5 --dataset *name of dataset containing labels in h5 file- --labels 10 34 101

To extract all labels from a file, store the .ply files in a specific folders and name them foo_xxx.ply:

python seg2mesh.py --path *path to segmentation file*.h5 --dataset *name of dataset containing labels in h5 file- --all --out-path *path to output folder- --out-name "foo"

• path: Path to the .h5 file to process.
• dataset: Default "label". Name of the h5 dataset to retrieve the labels from (all dataset can be listed by h5ls *path to segmentation file*.h5).

• labels: list of labels to extract (space separated), example= --labels 12 42 33 47.
• all: if passed all labels of the file are extracted.
• out-path: path to directory where to save ply file.
• out-name: use this as base name for output file(s).
• min-volume: minimal volume of label to be extracted (in voxels).
• batch: tab-delimited file containing list of time points and labels to process (see *Batch mode- below)
• batch-all: the script will extract all labels from all files which names are similar to the input files (i.e. all t/Txxxxx time points)
• multiprocessing: if called enables parallel processing of labels using all available cores.

• step-size: Step size for the marching cube algorithm, larger steps yield a coarser but faster result. Default 2 (voxel).

These modify the mesh before it is saved

• reduction: If reduction > 0 a decimation filter is applied. MaxValue 1.0 (100% reduction).
• smoothing: If called a Laplacian smoothing filter is applied.

This allows the processing of several .h5 files. Two modes are implemented:

• 1. Specific labels from specific files:

python seg2mesh.py --path *path to one of the segmentation file*.h5 --dataset *name of dataset containing labels in h5 file- --batch *path to tab delimited file with list of time points and labels*

The script will iterate over all time points listed in the batch file and for each, generate .ply files for all labels for that time. The *.ply- files are automatically sorted in subfolders (tXX)

The batch file should be tab-seprarated and contain two columns:

• 1st column: the time point to process encoded on two digits
• 2nd column: the labels to extract separated by a space

Example:

Frame   Labels
02      279 256
06      258 42 10 11

• 2. Extract all labels from all files

The files must contain a time point stamp of format T/tXXXXX (ex. t00012). The script will parse all files fitting this pattern and arrange the .ply files in subfolders (tXX).

Example:

python seg2mesh.py --path *path to segmentation file*.h5 --dataset *name of dataset containing labels in h5 file- --batch-all --out-path *path to output folder- --out-name "foo"