@@ -10,8 +10,8 @@ The persistent homology toolset provided here can be directly used for the shape
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 [data](https://erda.dk)("cristae_volume.npy","intracristal_volume.npy", and "mito_volume.npy")
- and unzip them into the "/binary_segmentation_masks" subfolder
- please download the [segmentation volumes](https://erda.ku.dk/workgroup/dikuUltrastructures/binary_segmentation_masks.zip)("cristae_volume.npy","intracristal_volume.npy", and "mito_volume.npy")
- and unzip them into the "/binary_segmentation_masks" subfolder.
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@@ -23,11 +23,16 @@ You won't need this part of the code, if your project is not related to mitochon
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 tiff file.
- 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.
Our registration of the Lausanne dataset (https://www.epfl.ch/labs/cvlab/data/data-em/) can be downloaded [here](https://erda.dk), and this is the volume which we segmented for analysis.
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Our registration of the Lausanne dataset (https://www.epfl.ch/labs/cvlab/data/data-em/) can be downloaded [here](https://erda.ku.dk/workgroup/dikuUltrastructures/lausanne.tif). This is the volume which we segmented for analysis.
## Alternative Link
An alternative version of our code, with all our data placed in the correct folders, can be downloaded [here](https://erda.ku.dk/workgroup/dikuUltrastructures/UCPH_IMAGE_persistent_homology.zip).