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Flood-Filling Networks for instance segmentation in 3d volumes.

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# Flood-Filling Networks

Flood-Filling Networks (FFNs) are a class of neural networks designed for
instance segmentation of complex and large shapes, particularly in volume
EM datasets of brain tissue.

For more details, see the related publications:

* https://arxiv.org/abs/1611.00421
 * https://doi.org/10.1101/200675

This is not an official Google product.

# Installation

No installation is required. To install the necessary dependencies, run:

```shell
  pip install -r requirements.txt
```

The code has been tested on an Ubuntu 16.04.3 LTS system equipped with a
Tesla P100 GPU.

# Training

FFN networks can be trained with the `train.py` script, which expects a
TFRecord file of coordinates at which to sample data from input volumes.

## Preparing the training data

There are two scripts to generate training coordinate files for
a labeled dataset stored in HDF5 files: `compute_partitions.py` and
`build_coordinates.py`.

`compute_partitions.py` transforms the label volume into an intermediate
volume where the value of every voxel `A` corresponds to the quantized
fraction of voxels labeled identically to `A` within a subvolume of
radius `lom_radius` centered at `A`. `lom_radius` should normally be
set to `(fov_size // 2) + deltas` (where `fov_size` and `deltas` are
FFN model settings). Every such quantized fraction is called a *partition*.
Sample invocation:

```shell
  python compute_partitions.py \
    --input_volume third_party/neuroproof_examples/validation_sample/groundtruth.h5:stack \
    --output_volume third_party/neuroproof_examples/validation_sample/af.h5:af \
    --thresholds 0.025,0.05,0.075,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9 \
    --lom_radius 24,24,24 \
    --min_size 10000
```

`build_coordinates.py` uses the partition volume from the previous step
to produce a TFRecord file of coordinates in which every partition is
represented approximately equally frequently. Sample invocation:

```shell
  python build_coordinates.py \
     --partition_volumes validation1:third_party/neuroproof_examples/validation_sample/af.h5:af \
     --coordinate_output third_party/neuroproof_examples/validation_sample/tf_record_file \
     --margin 24,24,24
```

## Sample data

We provide a sample coordinate file for the FIB-25 `validation1` volume
included in `third_party`. Due to its size, that file is hosted in
Google Cloud Storage. If you haven't used it before, you will need to
install the Google Cloud SDK and set it up with:

```shell
  gcloud auth application-default login
```

You will also need to create a local copy of the labels and image with:

```shell
  gcloud storage rsync --recursive --exclude ".*.gz" gs://ffn-flyem-fib25/ third_party/neuroproof_examples
```

## Running training

Once the coordinate files are ready, you can start training the FFN with:

```shell
  python train.py \
    --train_coords gs://ffn-flyem-fib25/validation_sample/fib_flyem_validation1_label_lom24_24_24_part14_wbbox_coords-*-of-00025.gz \
    --data_volumes validation1:third_party/neuroproof_examples/validation_sample/grayscale_maps.h5:raw \
    --label_volumes validation1:third_party/neuroproof_examples/validation_sample/groundtruth.h5:stack \
    --model_name convstack_3d.ConvStack3DFFNModel \
    --model_args "{\"depth\": 12, \"fov_size\": [33, 33, 33], \"deltas\": [8, 8, 8]}" \
    --image_mean 128 \
    --image_stddev 33
```

Note that both training and inference with the provided model are
computationally expensive processes. We recommend a GPU-equipped machine
for best results, particularly when using the FFN interactively in a Jupyter
notebook. Training the FFN as configured above requires a GPU with 12 GB of RAM.
You can reduce the batch size, model depth, `fov_size`, or number of features in
the convolutional layers to reduce the memory usage.

The training script is not configured for multi-GPU or distributed training.
For instructions on how to set this up, see the documentation on
[Distributed TensorFlow](https://www.tensorflow.org/deploy/distributed#replicated_training).

# Inference

We provide two examples of how to run inference with a trained FFN model.
For a non-interactive setting, you can use the `run_inference.py` script:

```shell
  python run_inference.py \
    --inference_request="$(cat configs/inference_training_sample2.pbtxt)" \
    --bounding_box 'start { x:0 y:0 z:0 } size { x:250 y:250 z:250 }'
```

which will segment the `training_sample2` volume and save the results in
the `results/fib25/training2` directory. Two files will be produced:
`seg-0_0_0.npz` and `seg-0_0_0.prob`. Both are in the `npz` format and
contain a segmentation map and quantized probability maps, respectively.
In Python, you can load the segmentation as follows:

```python
  from ffn.inference import storage
  seg, _ = storage.load_segmentation('results/fib25/training2', (0, 0, 0))
```

We provide sample segmentation results in `results/fib25/sample-training2.npz`.
For the training2 volume, segmentation takes ~7 min with a P100 GPU.

For an interactive setting, check out
[`ffn_inference_colab_demo.ipynb`](https://colab.sandbox.google.com/github/google/ffn/blob/master/notebooks/ffn_inference_colab_demo.ipynb).
This Colab notebook shows how to segment a single object with an explicitly defined
seed and visualize the results while inference is running.

Both examples are configured to use a 3d convstack FFN model trained on the
`validation1` volume of the FIB-25 dataset from the FlyEM project at Janelia.

# Further information

Please see `doc/manual.md`.

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