Light My Cells :Bright Field to Fluorescence¶
Imaging Challenge¶
Part of ISBI 2024ย in Athens, Greece!
๐ About¶
The Light My Cells France-Bioimaging challenge aims to contribute to the development of new image-to-image โdeep-labelโ methods in the fields of biology and microscopy.
๐งฌ Task¶
The main task is to predict the best-focused output-images of several fluorescently labelled organelles from label-free transmitted light input-images.
๐ฏ Aim¶
The aim of this challenge is to produce new open source methods which can handle a large acquisition variability:
- Z-focus,
- Multiple channels,
- Acquisition sites,
- Input-modalities (Bright Field, Phase Contrast & Differential Interference Contrast (DIC)),
- Instruments,
- Magnifications,
- Cellsย
- Markers
The high variability of the database (more information here) is possible thanks to the structuring role of the France-Bioimaging infrastructure.
๐งช Biological & medical motivations¶
In order to obtain fluorescence microscopy images, it is necessary to perform a manual biochemical labelling treatment โ time-consuming and costly โ over cells with specific fluorescent probes and dyes. But, the cells studied may themselves be perturbed by the fluorescence microscopy process, both by exposure to excitation light (phototoxicity) and by the probes themselves.
As phototoxicity increases with light exposure, it impairs long term imaging. Similarly, fluorophore dimming through photobleaching limits the signal-to-noise ratio of the images. Furthermore, adding markers is an invasive method. The fluorophore might hinder its target's molecular interactions and protein overexpression increases its concentration in the cytoplasm, disrupting regulation processes. Worse, the fluorophores themselves can be cytotoxic.
As fluorescence microscopy induces temporal and functional perturbations, it is thus crucial for live microscopy to limit the number of fluorescent probes used in an experiment.
On the contrary, label-free transmitted light microscopy such as bright field, phase contrast and DIC is non-invasive, phototoxicity is sharply reduced, and the signal quality is conserved throughout the acquisition.
The biological aim of this challenge is to recover fluorescence images in silico from bright field images.
๐ Technical motivations¶
We want to give a boost for multi-output deep learning methods based on
a single input, when the training database is made up of images that do
not always include all the required channels and have a high degree of
variability (e.g. magnification, depth of focus, numerical aperture).
This leads participants to develop in particular new architectures and
loss functions dedicated for sparse output.
The purpose is to offer a tool for biologists that can be robust on any
acquisition protocol and effective for the whole community, irrespective
of the size of the images, cell line, acquisition site, modality or
instrument. In order to assess the generalisability of the methods
developed, we will exclude one complete acquisition site from the
training database and leave it for the final evaluation. For the "Light
my cells" challenge, we want to evaluate the ability of the methods to
predict the best Z-focus plane for any organelle even in bad acquisition
conditions. To achieve this goal, participants will have the possibility
to perform data augmentation provided by the acquisitions. It consists
in large Z-stacks images of transmitted light microscopy containing a
majority of out focus planes. ย
We defined metrics for each of the 4 organelles and for each (0 to 5)
deviations of the focus plane to measure the ability to perform the
task. We will evaluate each participant on this 4x6 metrics matrix, and
the winners (information about awards
here) will be the ones with the best average of all
the metrics (more information
here).
Moreover, participants will get an additional bonus forย : code quality
and accessibility, lightweight deep learning model, short time of
training and prediction, and evaluation of the carbon footprint (more
information
here).
Among the current state-of-the-art approaches for image-to-image tasks
in bio-imaging are
"DeepHCS: Bright-field to fluorescence microscopy image
conversion using multi-task learning with adversarial losses for
label-free high-content screening" (2021) and
"Label-free prediction of cell painting from bright field
images" (2022), both of which focus their methodologies solely on
the use of the bright-field imaging modality, while
"In
Silico Labelling: Predicting Fluorescent Labels in Unlabeled Images"
(2018) uses the same three modalities as our approach.
While "DeepHCS" (2021) and "In Silico Labelling" (2018) use a wide range
of metrics to assess image quality, "Label-free prediction of cell
painting" (2022) uses a more restricted set of metrics.
However, these previous works present a very low diversity of
applications and do not provide an easily accessible database.
In addition, "DeepHCS" (2021) faces limitations due to the fixed sizes
and specific dyes of its database,"In Silico Labelling" (2018) uses
fixed formats that are not typical of those used in microscopy and
similarly, the authors of "Label-free prediction of cell painting"
(2022) admit limitations in the size and diversity of their database.
Nevertheless, a more extensive and publicly accessible JUMP-CP database
exists for cell painting, which can be used for pretraining theย 'Light
My Cells' challenge.
Yet to the best of our knowledge, the desired methods for the 'Light my
cells' challenge have no open source equivalent, and aspires to be
rooted with an open database and open algorithms.
France BioImaging¶
France BioImaging (FBI) is a National Infrastructure in Biology and Health (INBS), which federates 23 imaging acquisition sites distributed all over France. It is the French node of Euro-BioImaging and a partner of the Global BioImaging Network.
Its main role is to facilitate and coordinate research in biological imaging.
As an FBI project, the Light My Cells challenge want to innovate and give access toย 'deep-label' imaging methods and create new standards for the biologists.
Do you like this project ? Join the Light My Cells Challenge and dive into the future of imaging with us!¶
๐ข Institutions¶
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