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

• Data PNAS: VRL Data on google drive
• python main.py

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