Package: livecell
livecell-trackerLivecell-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 inhttps://github.com/xing-lab-pitt/xing-vimentin-dic-pipelinemaintained by Xing lab, and being added to this repo. Please check later in Nov. 2022 for a complete version with our manuscript.InstallationPytorchPlease refer toPytorch Official Websiteto receive most recent installation instructions. Here we simply provide two examples used in our cases.Install via pip:conda install pytorch torchvision -c pytorchOn our 2080Ti/3090 workstations and CUDA 11.7:conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidiacheck 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 requirementspip install -r requirements.txtDetectron2Please 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-onlyBuild from source:https://detectron2.readthedocs.io/en/latest/tutorials/install.html#build-detectron2-from-sourcegit clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2{avi, mp4} movie generationconda install -c conda-forge ffmpegExpected input/output for each submoduleNoteIf you already have satisfying segmentation models or segmentation results, you may skipAnnotationandSegmentationpart below.Annotationinput: raw image files
After annotating imaging datasets, you should have json files in COCO format ready for segmentation training.LabelmeApply 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.SegmentationSegmentation has two phase. If you already have pytorch or tensorflow models trained on your dataset, you may skip training phase.training phaseinput: COCO json filesoutput: pytorch model (.pth file)prediction phaseinput: raw images, a trained machine-learning based modeloutputs: SingleCellStatic json outputsTrackinput: SingleCellStaticcontourbounding boxoutput: SingleCellTrajectoryColletionholding a collection of singleCellTrajectory each containing single cell time-lapse datatrajectory-wise feature can be calculated after track stage or at trajectory stage.Trajectoryinput: SingleCellTrajectoryColletionoutput:Visualizertrack.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 topicsSingleCellStaticclass designed to hold all information about a single cell at some timepointattributestime pointid (optional)contour coordinatescell bounding boximg crop (lazy)feature maporiginal img (reference/pointer)SingleCellTrajectorytimeframe_setSingleCellTrajectoryCollection