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Flood filling networks for segmenting electron microscopy of neural tissue.

Project description


Flood filling networks for segmenting electron microscopy of neural tissue.

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Diluvian is an implementation and extension of the flood-filling network (FFN)
algorithm first described in [Januszewski2016]_. Flood-filling works by
starting at a seed location known to lie inside a region of interest, using a
convolutional network to predict the extent of the region within a small
field of view around that seed location, and queuing up new field of view
locations along the boundary of the current field of view that are confidently
inside the region. This process is repeated until the region has been fully

As of December 2017 the original paper's authors have released `their implementation <>`_.

Quick Start

This assumes you already have CUDA installed and have created a fresh
virtualenv. See `installation documentation <>`_
for detailed instructions.

Install diluvian and its dependencies into your virtualenv:

.. code-block:: console

pip install diluvian

For compatibility diluvian only requires TensorFlow CPU by default, but you
will want to use TensorFlow GPU if you have installed CUDA:

.. code-block:: console

pip install 'tensorflow-gpu==1.3.0'

To test that everything works train diluvian on three volumes from the
`CREMI challenge <>`_:

.. code-block:: console

diluvian train

This will automatically download the CREMI datasets to your Keras cache. Only
two epochs will run with a small sample set, so the trained model is not useful
but will verify Tensorflow is working correctly.

To train for longer, generate a diluvian config file:

.. code-block:: console

diluvian check-config > myconfig.toml

Now edit settings in the ``[training]`` section of ``myconfig.toml`` to your
liking and begin the training again:

.. code-block:: console

diluvian train -c myconfig.toml

For detailed command line instructions and usage from Python, see the
`usage documentation <>`_.

Limitations, Differences, and Caveats

Diluvian may differ from the original FFN algorithm or make implementation
choices in ways pertinent to your use:

* By default diluvian uses a U-Net architecture rather than stacked convolution
modules with skip links. The authors of the original FFN paper also now use
both architectures (personal communication). To use a different architecture,
change the ``factory`` setting in the ``[network]`` section of your config
* Rather than resampling training data based on the filling fraction
:math:`f_a`, sample loss is (optionally) weighted based on the filling
* A FOV center's priority in the move queue is determined by the checking
plane mask probability of the first move to queue it, rather than the
highest mask probability with which it is added to the queue.
* Currently only processing of each FOV is done on the GPU, with movement
being processed on the CPU and requiring copying of FOV data to host and
back for each move.

.. [Januszewski2016]
Michał Januszewski, Jeremy Maitin-Shepard, Peter Li, Jorgen Kornfeld,
Winfried Denk, and Viren Jain.
Flood-filling networks. *arXiv preprint*
*arXiv:1611.00421*, 2016.

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.. |license_badge|
:alt: License: MIT


0.0.6 (2018-02-13)

* Add CREMI evaluation command.
* Add 3D region filling animation.
* Fix region filling animations.
* F_0.5 validation metrics.
* Fix pip install.
* Many other fixes and tweaks (see git log).

0.0.5 (2017-10-03)

* Fix bug creating U-net with far too few channels.
* Fix bug causing revisit of seed position.
* Fix bug breaking sparse fill.

0.0.4 (2017-10-02)

* Much faster, more reliable training and validation.
* U-net supports valid padding mode and other features from original
* Add artifact augmentation.
* More efficient subvolume sampling.
* Many other changes.

0.0.3 (2017-06-04)

* Training now works in Python 3.
* Multi-GPU filling: filling will now use the same number of processes and
GPUs specified by ``training.num_gpus``.

0.0.2 (2017-05-22)

* Attempt to fix PyPI configuration file packaging.

0.0.1 (2017-05-22)

* First release on PyPI.

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