aliby-baby 0.1.17

Birth Annotator for Budding Yeast

Baby

Birth Annotation for Budding Yeast

Neural network code for segmenting buds from brightfield stacks.

Installation

BABY requires Python 3 and TensorFlow. For some versions of TensorFlow, you specifically need Python 3.6.

In any case, it is recommended that you install the package into a virtual environment (i.e., conda create if you are using Anaconda, or python3 -m venv otherwise).

By default, BABY will trigger installation of the latest version of TensorFlow. Our experience, however, is that performance is best with TensorFlow version 1.14. If you want to use this version, first install that in your virtual environment by running:

> pip install tensorflow==1.14

NB: To make use of a GPU you should also follow the set up instructions for installing tensorflow-gpu.

Install BABY by first obtaining this repository (e.g., git clone https://git.ecdf.ed.ac.uk/jpietsch/baby.git), and then using pip:

> pip install baby/

NB: If you are upgrading, then you may instead need to run: pip install -U baby/.

Developers: You may prefer to install an editable version:

> pip install -e baby/

Run using the Python API

Create a new BabyBrain with one of the model sets. The brain contains all the models and parameters for segmenting and tracking cells.

>>> from baby import BabyBrain, BabyCrawler, modelsets
>>> modelset = modelsets()['evolve_brightfield_60x_5z']
>>> brain = BabyBrain(**modelset)

For each time course you want to process, instantiate a new BabyCrawler. The crawler keeps track of cells between time steps.

>>> crawler = BabyCrawler(brain)

Load an image time series (from the tests subdirectory in this example). The image should have shape (x, y, z).

>>> from baby.io import load_tiled_image
>>> image_series = [load_tiled_image(
...     'tests/images/evolve_testG_tp{:d}_Brightfield.png'.format(t))
...     for t in range(1,6)]

Send images to the crawler in time-order (here a batch of size 1). We additionally request that outlines are optimised to edge predictions, and that lineage assignments, binary edge-masks and volume estimates (using the conical method) should be output at each time point.

>>> segmented_series = [crawler.step(
...     img[None, ...], refine_outlines=True, assign_mothers=True,
...     with_edgemasks=True, with_volumes=True)
...     for img, _ in image_series]

Finally, save the segmentation outlines, labels, volumes and lineage assignments as an annotated tiled png:

>>> from baby.io import save_tiled_image
>>> for t, s in enumerate(segmented_series):
...     save_tiled_image(255 * s[0]['edgemasks'].astype('uint8').transpose((1, 2, 0)),
...     '../segout_tp{:d}.png'.format(t + 1),
...     {k: s[0][k] for k in ('cell_label', 'mother_assign', 'volumes')})

Run via a server

Once installed, you should be able to start a server to accept segmentation requests using:

> baby-phone

or on windows:

> baby-phone.exe

Server runs by default on http://0.0.0.0:5101. HTTP requests need to be sent to the correct URL endpoint, but the HTTP API is currently undocumented. The primary client implementation is in Matlab.

Jupyter notebooks

Training scripts are saved in Jupyter notebooks in the notebooks folder. To maintain the repository in a clean state, it's probably best to copy these to another directory for routine use. If you want to share a notebook, you can then specifically add it back to the repository at a useful checkpoint.

On how to retrain data

As of mid-2022 we aim to transition to tensorflow 2 (and then to pytorch). This means re-training all networks. We first fetch our data from skye and regenerate the train-val-test pair sets using TrainValTestPairs:

from pathlib import Path
from baby.io import TrainValTestPairs

training_data_path = Path("/home/alan/Documents/dev/training/training-images/")
tvt = TrainValTestPairs()
tvt.add_from(training_data_path / "traps-prime95b-60x")
<!-- tvt.add_from(training_data_path / "traps-evolve-60x") -->