livecell 0.0.2

live-cell analysis package including instance segmentation and tracker

livecell-tracker

Livecell-tracker is a pure python framework for extracting (segmenting and tracking) sinlge cell trajectories from long live-cell imaging data, and then computing as well as analyzing single cell features in latent space.

This is a placeholder for livecell-tracker future releases. Currently this repo showcases a basic use case to segment images, track cells with opencv/SORT/btrack and generate cell features in our CX-A label-free dataset.
The majority of our analysis methods/notebooks are in https://github.com/xing-lab-pitt/xing-vimentin-dic-pipeline maintained by Xing lab, and being added to this repo. Please check later in Nov. 2022 for a complete version with our manuscript.

Installation

Pytorch
Please refer to Pytorch Official Website to receive most recent installation instructions. Here we simply provide two examples used in our cases.

Install via pip:
conda install pytorch torchvision -c pytorch

On our 2080Ti/3090 workstations and CUDA 11.7:
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

check if you are using cuda (refer to pytorch docs for TPU or other devices): torch.cuda.is_available(), torch.cuda.current_device(), torch.cuda.device_count()

General package requirements
pip install -r requirements.txt

Detectron2

Please refer to latest detectron2 documentation to install detectron2 for segmentation if you cannot build from source with the following commands.

Prebuilt (Easier and preferred by us):
https://detectron2.readthedocs.io/en/latest/tutorials/install.html#install-pre-built-detectron2-linux-only

Build from source:
https://detectron2.readthedocs.io/en/latest/tutorials/install.html#build-detectron2-from-source

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

{avi, mp4} movie generation conda install -c conda-forge ffmpeg

Expected input/output for each submodule

Note
If you already have satisfying segmentation models or segmentation results, you may skip Annotation and Segmentation part below.

Annotation

input: raw image files After annotating imaging datasets, you should have json files in COCO format ready for segmentation training.

Labelme

Apply labelme to your datasets following our annotation protocol.

Convert labelme json to COCO format.

A fixed version of labelme2coco implementation is included in our package. Please refer to our tutorial on how to convert your labelme json to COCO format.
For CVAT, please export the annotation results as COCO, as shown in our annotation protocol.

Segmentation

Segmentation has two phase. If you already have pytorch or tensorflow models trained on your dataset, you may skip training phase.

training phase

input: COCO json files

output: pytorch model (.pth file)

prediction phase

input: raw images, a trained machine-learning based model
outputs: SingleCellStatic json outputs

Track

input: SingleCellStatic

• contour
• bounding box

output: SingleCellTrajectoryColletion

• holding a collection of singleCellTrajectory each containing single cell time-lapse data
• trajectory-wise feature can be calculated after track stage or at trajectory stage.

Trajectory

input: SingleCellTrajectoryColletion

output:

Visualizer

track.movie: generate_single_trajectory_movie()

visualizer: viz_traj, viz_traj_collection

{Documentation placeholder} [Move to docs/ and auto generate by readthedocs]

Analyze trajectories based on specific research topics

SingleCellStatic

class designed to hold all information about a single cell at some timepoint
attributes

• time point
• id (optional)
• contour coordinates
• cell bounding box
• img crop (lazy)
• feature map
• original img (reference/pointer)

SingleCellTrajectory

• timeframe_set

SingleCellTrajectoryCollection