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Time-lapse 3D Cell Analysis

This repository contains the source code for the paper "Deep Learning Enabled Time-Lapse 3D Cell Analysis", where we introduce an end-to-end analysis method for boundary tagged 3D cell images.

This whole cell analysis mainly contains three steps: 3D cell segmentation, cellular/sub-cellular feature extraction, and cell tracking. These three components can be used together or separately.

Code

Installation

You need to install SimpleITK, pytorch, scikit-image, opencv, DenseInferenceWrapper

3D Cell Segmentation

Testing

Histogram Match (with PNAS and Purdue Data)

  • python hist_match.py
  • celldataset.cell_testing('/source/data/celldata/') contains the background subtracted images
  • celldataset.cell_training('/source/data/PNAS/') takes the PNAS dataset for training
  • results in hist_match folder (z interpolation is done 5 times)

Predict

  • python predict.py
  • user model_last.tar file
  • generates a probability map in prob_map folder
  • uses pytorch, torchvision and scikit image

Post Processing

  • python postprocessing.py
  • Enter Seeds as 15 and the threshold for black voxels as 0.05
  • Hyperparams are in main function
  • result folder has the output

Training

3D Cellular and Sub-cellular feature extraction

  • cd sub_celluar
  • python feature.py
  • Input should be the individual segmented 3D TIFF image stack
  • This will compute features include Cell volumn (in voxels), neighboring (Adjacent) Cells, 3D cell surface, three cell wall junction points, cell center, anticlinal wall segments

3D Cellular and Sub-cellular feature extraction

  • cd Tracking
  • python track.py
  • Input should be a sequence of segmented TIFF 3D image stacks. Input names should be T+3 digit number to denote time instances.
  • It will output tracked TIFF files and a txt files containing all cell/nuclei tracks. The details of the format of txt file, please refer to celltrackingchallenge
  • This can be used for tracking both cells and nuclei