ALIBY (Analyser of Live-cell Imaging for Budding Yeast)

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End-to-end processing of cell microscopy time-lapses. ALIBY automates segmentation, tracking, lineage predictions, post-processing and report production. It leverages the existing Python ecosystem and open-source scientific software available to produce seamless and standardised pipelines.

Quickstart Documentation

Installation of VS Studio Native MacOS support for is under work, but you can use containers (e.g., Docker, Podman) in the meantime.

For analysing local data

pip install aliby # aliby[network] if you want to access an OMERO server

See our installation instructions for more details.


aliby-run --expt_id EXPT_PATH --distributed 4 --tps None

And to run Omero servers, the basic arguments are shown:

aliby-run --expt_id XXX --host SERVER.ADDRESS --user USER --password PASSWORD

The output is a folder with the original logfiles and a set of hdf5 files, one with the results of each multidimensional inside.

Using specific components

Access raw data

ALIBY’s tooling can also be used as an interface to OMERO servers, taking care of fetching data when needed.

from import Dataset
from import Image

server_info= {
           "host": "host_address",
           "username": "user",
           "password": "xxxxxx"}
expt_id = XXXX
tps = [0, 1] # Subset of positions to get.

with Dataset(expt_id, **server_info) as conn:
   image_ids = conn.get_images()

#To get the first position
with Image(list(image_ids.values())[0], **server_info) as image:
   dimg =
   imgs = dimg[tps, image.metadata["channels"].index("Brightfield"), 2, ...].compute()
   # tps timepoints, Brightfield channel, z=2, all x,y

Tiling the raw data

A Tiler object performs trap registration. It may be built in different ways but the simplest one is using an image and a the default parameters set.

from aliby.tile.tiler import Tiler, TilerParameters
with Image(list(image_ids.values())[0], **server_info) as image:
    tiler = Tiler.from_image(image, TilerParameters.default())

The initialisation should take a few seconds, as it needs to align the images in time.

It fetches the metadata from the Image object, and uses the TilerParameters values (all Processes in aliby depend on an associated Parameters class, which is in essence a dictionary turned into a class.)

Get a timelapse for a given trap

fpath = "h5/location"

trap_id = 9
trange = list(range(0, 30))
ncols = 8

riv = remoteImageViewer(fpath)
trap_tps = riv.get_trap_timepoints(trap_id, trange, ncols)

This can take several seconds at the moment. For a speed-up: take fewer z-positions if you can.

Get the traps for a given time point

Alternatively, if you want to get all the traps at a given timepoint:

timepoint = 0
seg_expt.get_tiles_timepoints(timepoint, tile_size=96, channels=None,


See CONTRIBUTING on how to help out or get involved.