DISCo: Deep learning, Instance Segmentation and Correlations for cell segmentation in calcium imaging
This is a method to perform the cell segmentaiton step in caclium imaging analysis, which uses the temporal information from caclium imaging videos in form of correlations, and combines a deep learning model with an instance segmentation algorithm.
"DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging", E. Kirschbaum, A. Bailoni, F. A. Hamprecht, arXiv preprint arXiv:1908.07957, 2019. [pdf]
- Python 3.6 (or later): we recommend installing it with Anaconda
- PyTorch 1.0 (or later)
- GASP
- inferno 0.3 (or later)
- Download or clone this repository
- Install GASP as described here
- Get inferno as described here
- Download the neurofinder training and test data from here
- Extract the neurofinder data into HDF5 files:
- create for each neurofinder video a HDF5 file with a dataset named 'video' containing the video with shape time x X x Y
- create a file named BF_labels.h5 containing the foreground-background labels for each video
- create a file summary_images.h5 containing the mean intensity projection for each video
- create a file gt_segmentations.h5 containing the instance labels for each video
Option | Name | Description |
---|---|---|
-p |
path | Path to the folder containing the .h5 video files and the ground truth segmentations |
-m |
mode | Decide whether a single network is trained on all videos ('disco') or individual networks on the five dataset series ('discos') |
-gpu |
gpu ID | Select the GPU to train on. |
-a |
additional ending | Additional ending to the output filename. |
Example:
python run.py -p ../neurofinder_videos/ -m disco -gpu 1