in-silico-labeling

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Code for In silico labeling: Predicting fluorescent labels in unlabeled images

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In silico labeling: Predicting fluorescent labels in unlabeled images

This is the code for In silico labeling: Predicting fluorescent labels in unlabeled images. It is the result of a collaboration between Google Accelerated Science and two external labs: the Lee Rubin lab at Harvard and the Steven Finkbeiner lab at Gladstone. See also our blog post and our full dataset, including many predictions we couldn't fit in the paper.

This code in this repository can be used to run training and inference of our model on a single machine, and can be adapted for distributed training. It also contains a set of weights created by training the model on the data in Conditions A, B, C, and D from the paper.

This README will explain how to:

  1. Restore the model from the provided checkpoint and run inference on an image from our test set.
  2. Train the pre-trained model on a new dataset (Condition E).

Please note, the example in this README deviates from the way transfer learning is demonstrated in the paper. In the paper, the model is trained once on all datasets, including Condition E; this is also called multi-task learning. In this guide, we're starting with a pre-trained model on Conditions A, B, C, and D, and incrementally training it on Condition E. The benefit of the latter technique is it requires less time to learn a new task, but it is likely to overfit if overtrained (see below).

The model here differs from the model described in the paper in one significant way: this model does an initial linear projection to 16 features per pixel, where the model in the paper does not have this step. This allows the model to take as input z-stacks with varying numbers of z depths; the only thing that needs to be relearned is the initial linear projection. Here, we'll use it to take full advantage of the 26 z depths in Condition E (the other conditions have 13 z depths).

You can also find the complete set of preprocessed data used for the paper, along with predictions, here.

This is not an official Google product.

Disclaimers

This code is not being developed

This code exists primarily to let readers reproduce some of the results in the Cell paper. We welcome pull requests, but sadly don't have the time to make improvements ourselves. If you want an easy-to-use, state-of-the-art, image-to-image predictor for your microscopy problem, you should probably look elsewhere.

This code is not suitable for de novo training

This code does CPU fine-tuning on a single machine, while for the paper we used a distributed system consisting of many CPU workers. It would take a long time to train from scratch on a single workstation, and in any case training on CPUs is a much worse option now that it used to be - for a network like this it would make more sense to train on a GPU or TPU.

Installation

Code

The code requires Python 3 and depends on NumPy, TensorFlow, and OpenCV. You can install TensorFlow using this guide, which will also install NumPy. If you follow that guide you will have installed

pip3
and you'll be able to install OpenCV with the command:
pip3 install
--upgrade opencv-python
.

We're using Bazel to build the code and run tests. Though not strictly necessary, we suggest you install it (the rest of this README will assume you have it).

This code has been tested in Debian 10 with TensorFlow 1.9 on a machine with 64 GB RAM. It is not optimized for memory use, and has been reported to fail with a Python

MemoryError
on a machine with 16 GB RAM. Based on tests with
ulimit
, you may be able to squeak by with 32 GB RAM, but some users have reported needing 64 GB.

Data

We'll work with the data sample in

data_sample.zip
(Warning: 2 GB download) and a checkpoint from the pre-trained model in
checkpoints.zip
. If you have
gsutil
installed, you can also use the commands:

gsutil -m cp gs://in-silico-labeling/checkpoints.zip .
gsutil -m cp gs://in-silico-labeling/data_sample.zip .

The rest of this README will assume you've downloaded and extracted these archives to

checkpoints/
and
data_sample/
.

Running the pre-trained model.

Training and inference are controlled by the script

isl/launch.py
. We recommend you invoke the script with Bazel (see below), because it will handle dependency management for you.

The

checkpoints.zip
file contains parameters from a model trained on Conditions A, B, C, and D. To run the model on a sample from Condition B, run this command from the project root:
export BASE_DIRECTORY=/tmp/isl
bazel run isl:launch -- \
  --alsologtostderr \
  --base_directory $BASE_DIRECTORY \
  --mode EVAL_EVAL \
  --metric INFER_FULL \
  --stitch_crop_size 1500 \
  --restore_directory $(pwd)/checkpoints \
  --read_pngs \
  --dataset_eval_directory $(pwd)/data_sample/condition_b_sample \
  --infer_channel_whitelist DAPI_CONFOCAL,MAP2_CONFOCAL,NFH_CONFOCAL

If you get a syntax error, make sure you're using Python 3, not Python 2.

In the above:

  1. BASE_DIRECTORY
    is the working directory for the model. It will be created if it doesn't already exist, and it's where the model predictions will be written. You can set it to whatever you want.
  2. alsologtostderr
    will cause progress information to be printed to the terminal.
  3. stitch_crop_size
    is the size of the crop for which we'll perform inference. If set to 1500 it may take an hour on a single machine, so try smaller numbers first.
  4. infer_channel_whitelist
    is the list of fluorescence channels we wish to infer. For the Condition B data, this should be a subset of
    DAPI_CONFOCAL
    ,
    MAP2_CONFOCAL
    , and
    NFH_CONFOCAL
    .

If you run this command, you should get a

target_error_panel.png
that looks like this:

Initial predictions for Condition B

Each row is one of the whitelisted channels you provided; in this case it's one row for each of the

DAPI_CONFOCAL
,
MAP2_CONFOCAL
, and
NFH_CONFOCAL
channels. The boxes with the purple borders show the predicted images (in this case the medians of the per-pixel distributions). The boxes with the teal borders show the true fluorescence images. The boxes with the black borders show errors, with false positives in orange and false negatives in blue.

The script will also generate a file called

input_error_panel.png
, which shows the 26 transmitted light input images along with auto-encoding predictions and errors. For this condition, there were only 13 z-depths, so this visualization will show each z-depth twice.

Training the pre-trained model on a new dataset.

Condition E contains a cell type not previously seen by the model (human cancer cells), imaged with a transmitted light modality not previously seen (DIC), and labeled with a marker not previously seen (CellMask). We have two wells in our sample data (B2 and B3), so let's use B2 for training and B3 for evaluation.

To see how well the model can predict labels on the evaluation dataset before training, run:

export BASE_DIRECTORY=/tmp/isl
bazel run isl:launch -- \
  --alsologtostderr \
  --base_directory $BASE_DIRECTORY \
  --mode EVAL_EVAL \
  --metric INFER_FULL \
  --stitch_crop_size 1500 \
  --restore_directory $(pwd)/checkpoints \
  --read_pngs \
  --dataset_eval_directory $(pwd)/data_sample/condition_e_sample_B3 \
  --infer_channel_whitelist DAPI_CONFOCAL,CELLMASK_CONFOCAL \
  --noinfer_simplify_error_panels

This should produce this target error panel:

Initial predictions for Condition E

This is like the error panels above, but the first row is

DAPI_CONFOCAL
and the second is
CELLMASK_CONFOCAL
. And because we used
noinfer_simplify_error_panels
it includes more statistics of the pixel distribution. Previously, there was one purple-bordered box which showed the medians of the pixel distributions. Now there are four purple-bordered boxes which show, in order, the mode, median, mean, and standard deviation. There are now three boxes with black borders, showing the same error visualization as before, but for the mode and mean as well as the median. The white-bordered boxes are a new kind of error visualization, in which colors on the grayline between black and white correspond to correct predictions, orange corresponds to a false positive, and blue corresponds to a false negative. The final mango-bordered box is not informative for this exercise.

The pre-trained model hasn't seen the Condition E dataset, so we should expect its predictions to be poor. Note, however, there is some transfer of the nuclear label even before training.

You can find the input images, consisting of a z-stack of 26 images, here (Warning: 100 MB download).

Training

We can train the network on the Condition E data on a single machine using a command like:

export BASE_DIRECTORY=/tmp/isl
bazel run isl:launch -- \
  --alsologtostderr \
  --base_directory $BASE_DIRECTORY \
  --mode TRAIN \
  --metric LOSS \
  --master "" \
  --restore_directory $(pwd)/checkpoints \
  --read_pngs \
  --dataset_train_directory $(pwd)/data_sample/condition_e_sample_B2

By default, this uses the ADAM optimizer with a learning rate of 1e-4. If you wish to visualize training progress, you can run TensorBoard on

BASE_DIRECTORY
:
tensorboard --logdir $BASE_DIRECTORY

You should eventually see a training curve that looks like this:

Train curve

After 50,000 steps, which takes about a week on a 32-core machine, predictions on the eval data should have substantially improved. You can run this command to generate predictions:

export BASE_DIRECTORY=/tmp/isl
bazel run isl:launch -- \
  --alsologtostderr \
  --base_directory $BASE_DIRECTORY \
  --mode EVAL_EVAL \
  --metric INFER_FULL \
  --stitch_crop_size 1500 \
  --read_pngs \
  --dataset_eval_directory $(pwd)/data_sample/condition_e_sample_B3 \
  --infer_channel_whitelist DAPI_CONFOCAL,CELLMASK_CONFOCAL \
  --noinfer_simplify_error_panels

Note, we've dropped the

restore_directory
argument, so the model will run inference using the latest checkpoint it finds in
BASE_DIRECTORY
.

Here's what the predictions should look like on the evaluation data:

Predictions for the Condition E evaluation well (B3) after 50K steps

Note, there is a bug in the normalization of the

DAPI_CONFOCAL
channel causing it to have a reduced dynamic range in the ground-truth image. Comparing the initial nuclear predictions with these, it is clear the network learned to match the reduced dynamic range.

For reference, here's what predictions should look like on the train data:

Predictions for Condition A

Note, if we train too long the model will eventually overfit on the train data and predictions will worsen. This was not an issue in the paper, because there we simultaneously trained on all tasks, so that each task regularized the others.

Citing the code

If you use this code, please cite our paper:

Christiansen E, Yang S, Ando D, Javaherian A, Skibinski G, Lipnick S, Mount E, O'Neil A, Shah K, Lee A, Goyal P, Fedus W, Poplin R, Esteva A, Berndl M, Rubin L, Nelson P, Finkbeiner S. In silico labeling: Predicting fluorescent labels in unlabeled images. Cell. 2018

BibTeX:

@article{christiansen2018isl,
  title={In silico labeling: Predicting fluorescent labels in unlabeled images},
  author={Christiansen, Eric M and Yang, Samuel J and Ando, D Michael and Javaherian, Ashkan and Skibinski, Gaia and Lipnick, Scott and Mount, Elliot and O’Neil, Alison and Shah, Kevan and Lee, Alicia K and Goyal, Piyush and Fedus, William and Poplin, Ryan and Esteva, Andre and Berndl, Marc and Rubin, Lee L and Nelson, Philip and Finkbeiner, Steven},
  journal={Cell},
  year={2018},
  publisher={Elsevier}
}

TODOs

  1. Fix the tests.
  2. Fix the
    DAPI_CONFOCAL
    normalization bug for the Condition E data. Note: This bug was introduced after data was generated for the paper.

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