CNNectome is a collection of scripts for building, training and validating Convolutional Neural Networks (CNNs) for Connectomics. It relies on gunpowder and Tensorflow.
Docker images can be built with the Dockerfile in docker/ or downloaded from
this dockerhub.
Distribution via pypi will be available soon, but for now you can install the repo by cloning it and then installing via
pip install .
Many parts of this repo depend on specific data for organelle and synapse segmentations as laid out below. In order to
run data-dependent parts of the pipeline you will have to download the relevant data and configure the repo to point
to your download directories.
An example config file is included in etc/config_local.ini. By calling init_CNNectome_config (or calling any script
trying to access the configuration) the example config file will be copied to the system-dependent default location
for config files (if it doesn't yet exist).
Preprint: Larissa Heinrich, Davis Bennett, David Ackerman, Woohyun Park, John Bogovic, Nils Eckstein, Alyson Petruncio, Jody Clements, C. Shan Xu, Jan Funke, Wyatt Korff, Harald F. Hess, Jennifer Lippincott-Schwartz, Stephan Saalfeld, Aubrey V. Weigel, COSEM Project Team. Automatic whole cell organelle segmentation in volumetric electron microscopy. bioRxiv (2020) 10.1101/2020.11.14.382143
Find links to all the code involved in this poject at github.com/janelia-cosem/heinrich-2021a.
The training data for organelle segmentation can be downloaded from s3://janelia-cosem-publications/heinrich-2021a/
- specifically you need the raw data and groundtruth blocks for
jrc_hela-2,jrc_hela-3,jrc_jurkat-1,jrc_macrophage-2andjrc_sum159-1. Their directories should be located in data_path. Further, some of the metadata for the groundtruth blocks is organized in a mongo database. Since you only need read access here, you can use the public facing instance of our database (mongodb+srv://cosemRead:xYF1fCyVHrIfujKm@cosem-public.5a8vu.mongodb.net, configurable via database-public).
The file CNNectome/training/unet_template.py can serve as a starting point for setting up your own trainings.
The setups from the paper can be found in a separate repo:
github.com/janelia-cosem/training_setups with all checkpoints
used to reconstruct the cells on openorganelle in a corresponding s3 bucket.
These can be distributed across several directories, configured via a comma-separated list in training_setups_paths.
All evaluations presented in the paper (and more) are also saved in the same database as the metadata for
groundtruth blocks (database-public). To add evaluations of your own
(CNNectome/validation/organelles/run_evaluation.py) you'll need your own mongodb instance to which
you have write access; this is configurable via database-private. Of course you'll also need the validation crops (which are part of the groundtruth) in data_path as well as
their metadata in database-public.
In order to reproduce the comparisons presented in the paper, in addition to database-public and
training_setups_paths, you'll need to download the results of the manual evaluation procedures from
the s3 bucket: s3://janelia-cosem-publications/heinrich-2021a/evaluations. evaluation_path should point
directly to that directory.
The scripts for running and visualizing the comparisons are in
CNNectome/validation/organelles/comparisons.py and CNNectome/visualization/organelles/plot_comparisons.py,
respectively.
coming soon: find refined segmentations in data_path instead of separate refined_seg_path.
###Other notes
The names used for organelle classes in this repo and in the paper differ. The mapping can be found in
CNNectome/utils/hierarchy.py.
The preprocessing for the raw data has been improved since these setups have been trained. If you start training from scratch consider using the raw data linked on openorganelle.
Instructions coming soon