Yeastvision

A GUI-based framework for deep-learning enabled segmentation, tracking, time-series analysis of the full Saccharomyces cerevisiae lifecycle

Read the paper Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Developed at the Miranda Laboratory at NCSU

Yeastvision tracks a single yeast cell throughout all life cycle stages

Key Features

• FIEST Tracking Algorithm: Enhance time-series resolution up to 16x using a generative video interpolation model[1], used to enhance cell-tracking and lineage reconstruction
• Load, analyze, and segment multiple experiments containing numerous phase/flourescent channels
• Segment cytoplasm, vacuoles, buds, mating, and sporulating yeast cells
• Extract and plot time-series data in the GUI
  
  
  
  
  
  
  

Installation

Local installation (< 2 minutes)

System requirements

This package supports Linux, Windows and Mac OS (versions later than Yosemite). GPU support is available for NVIDIA GPU's. A GPU is recommended, but not required, to run yeastvision

Instructions

yeastvision is ready to go for cpu-usage as soon as it downloaded. GPU-usage requires some additional steps after download. To download:

1. Install an Anaconda distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
2. Open an anaconda prompt/command prompt
3. If you have an older yeastvision environment you should remove it with conda env remove -n yeastvision before creating a new one.
4. Create a new environment with conda create --name yeastvision python=3.10.0.
5. Activate this new environment by running conda activate yeastvision
6. To download our package plus all dependencies, run python -m pip install yeastvision on Windows and python3 -m pip install yeastvision on Linux, Ubuntu, and Mac OS.

You should upgrade the yeastvision package periodically as it is still in development. To do so, run the following in the environment:

python -m pip install yeastvision --upgrade

for Windows or

python3 -m pip install yeastvision --upgrade

See Troubleshooting: Common Problems for potential install errors.

Using yeastvision with Nvidia GPU: PyTorch Configurations

To use your NVIDIA GPU with python, you will first need to install a NVIDIA driver for your GPU. Once downloaded, ensure that your GPU is detected by running nvidia-smi in the terminal.

yeastvision relies on pytorch for implementation of the deep-learning models, which we will need to configure for gpu usage. Ensure your yeastvision conda environment is active for the following commands.

First, we need to remove the CPU version of torch:

pip uninstall torch

And the cpu version of torchvision:

pip uninstall torchvision

Now install torch and torchvision for CUDA version 11.3. Ensure that your nvidia drivers are up to date for version 11.3 by running nvidia-smi and check that a version 11.3 or greater is displayed in the top right corner of the output table.

conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch

After install you can check conda list for pytorch, and its version info should have cuXX.X, not cpu.

Running yeastvision

Quickstart

Activate the correct conda environment. In the command prompt, run either

• yeastvision -test to open the GUI with our sample 10-frame movie.
• yeastvision to open a blank GUI window. Drag and drop directory containing 2D-image files into the GUI to begin segmenting, labeling, or processing

Yeastvision accepts directories of image files, loaded through drag-and-drop or the file menu. Each file in the directory should contain only a single 2D image, named with a standard image file extension.

Note: As you segment, interpolate, or process images, GUI-generated labels and images are stored in the loaded directory as .npz files. Deleting these files results in loss of the data.

Yeastvision expects directories to contain only files with valid image extensions or .npz files created in previous yeastvision sessions.

Important: Ensure that all images in the same channel have the same dimensions.

To load directories containing additional image channels and pre-generated masks, name your files properly:

For experiments containing multiple channels and pre-generated labels, our GUI loading capabilities make it easy to:

• Track pre-generated labels (a nucleus label, for example)
• extract and plot fluorescence intensities
• interpolate additional fluorescence channels.

When additional channels/masks are present in the directory, files should be named accordingly to their channel and mask type:

1. Each data type in the directory should have a standard id that directly precedes the file extension (ex: _phase.tif, _channel2.png, _mask1.tif)
2. Any image that acts as a label should have _mask in the id (ex: _mask_nucleus.tif, _mask_cytoplasm.jpg).
3. Ensure that each id has the same number of time points: Having 5 phase images but only 4 fluorescence images will raise an error.

Here is an example of a directory with two time points, two channels, and two pre-generated labels, sorted by name:

im001_channel1.tif, im001_channel2.tif,
im001_mask1.tif, im001_mask2.tif,
im002_channel1.tif, im002_channel2.tif,
im002_mask1.tif, im002_mask2.tif

GUI Features

Segmentation

Yeastvision contains models to accurately segment yeast in all stages of their lifecycle. Simply choose one of the following models from the model dropdown and click run.

Pixel flow-based models

Model	Segments
proSeg	proliferating cells (general cytoplasm segmentation)
spoSeg	sporulating cells
matSeg	mating cells
budSeg	bud-necks

Yeastvision contains models and tracking algorithms to analyze all stages of the yeast lifecycle

Model Retraining

1. Ensure that training data is loaded into the current experiment
2. Select the model to be retrained from the mainscreen model dropdown
3. Click Menu->Models->Retrain
4. Ensure training data is correct and choose model suffix (default is date-time)
5. Select hyperparameters (default should work for most use cases)
6. Train the model, using terminal to gauge progress.
7. The custom model will auto-run on the next available image in the training set, if there is not a mask already on this image.
8. If you are happy with the new model, go to Menu->Models->Load Custom Models, and the model will be added to the model dropdown. Otherwise, retrain with new data

Retraining Tips

• Even though it possible to retrain using only CPU, training takes very long without a GPU
• When you are initially producing a training set, leave some blank masks towards the end of the movie so that the training procedure has room to auto-run
• The path to the new weights will be printed on the terminal.
• Ensure that the fullname of the retrained model is present in the weights filename upon trying to load it via the models menu. This ensures that GUI can associate the weights with the correct model architecture

Timeseries analysis: interpolation, tracking, and plotting

1. Optional: Interpolate images to increase resolution, generating intermediate frames that improve tracking accuracy
2. Segment and track the interpolated frame. Tracking automatically generates a cell data table that includes various morphological and image properties of each cell over each frame in the movie.
3. Optional: reconstruct proliferating cell lineages
4. Remove interpolated frames from the mask, so that only frames present in the original movie exist in the final tracked movie
5. Produce some initial plots of the data using the 'show plot window' button. This will allow you to view single cell and population averages over time.

Keyboard Shortcuts

Command	Function
up/down	scroll through channnels
cntrl + up/down	Scroll through labels
right/left arrows	scroll through timeseries
cntrl + right/left	scroll through timeseries by 3
O	outline Drawing
B	brush Drawing
E	eraser
.	increment brush size
,	decrecement brush size
Delete/Backspace	Delete Selected Cell
c	show current label contours
f	toggle probability (if present)
space bar	toggle mask display
p	show plot window

Troubleshooting: Common Problems

Problem	Solution
ModuleNotFoundError: No module named 'chardet'	run pip install chardet
Cannot scroll through images/masks on the display	Click on the display to bring focus back to this widget
Loaded images without masks but cannot draw	An existing label must be present to draw: Add a blank label with File -> Add Blank Label

References

[1] Huang, Z., Zhang, T., Heng, W., Shi, B., & Zhou, S. (2022). Real-time intermediate flow estimation for video frame interpolation. In Proceedings of the European Conference on Computer Vision (ECCV).