3D Shape Analysis using Persistent Homology
This is the official code accompanying our paper Extracting Mitochondrial Cristae Characteristics from 3D Focused Ion Beam Scanning Electron Microscopy Data.
Persistent Homology Toolset
The persistent homology toolset provided here can be directly used for the shape analysis of other object types, and is not restricted to the mitochondria and their cristae.
Open "3D Shape Analysis using Persistent Homology.ipynb" to get started.
If you would like to run our real world example locally on your machine:
- please download the segmentation volumes ("cristae_volume.npy", "intracristal_volume.npy", and "mito_volume.npy")
- and unzip them into the "/binary_segmentation_masks" subfolder.
Segmentation Model
For completeness, we also included our multiplanar segmentation model for mitochondria and cristae in the "/other/segmentation_model" folder.
You won't need this part of the code, if your project is not related to mitochondria and cristae, or if you have your own segmentations already.
If you do decide to use our segmentation code:
- You need to first perform image registration to align the slices in your image volume.
- Place the registered image volume in "/raw_data/test" subfolder as a tif file.
- Adjust the filename in the loading function in "segmentation_main".
Please note that the raw output needs to be binarized and cleaned up. We do not provide the code for binarization and clean up, because the best procedure is likely to be specific to each image volume and should be simple enough to figure out.
The segmentation can take several days depending on the size of your image volume and your PC's computing power.
Alternative Link
An alternative version of our code, with all our data placed in the correct folders, can be downloaded here.