Connor323 / Cancer-Cell-Tracking

This project focuses on tracking cancer cell based on Fuhua et al. [1], which used watershed algorithm to segment cells and built a feature vector for cell tracking including the information of position, shape, spatial distribution and texture.

Cancer-Cell-Tracking

Python implementation on tracking cancer cell based on Li et al., which used watershed algorithm to segment cells and built a feature vector for cell tracking including the information of position, shape, spatial distribution and texture.

Usage

1. The data can be found at Cell Tracking Challenge Website.

2. ipython notebook: to better show the algorithm step by step, besides the python scripts, I also create a ipython notebook to visualize the interim results.

3. Some explanation of the scripts:

main.py # the main procedure including all steps.

adaptivethresh.py # compute adaptive thresholding of image sequence in order to generate binary image for Nuclei segmentation.

gvf.py # compute gradient vector field (GVF) to find the seeds for following watershed.

watershed.py # segment cells

graph_construction.py # generate a neighboring graph contraction using Delaunary Triangulation.

matching.py # calculate feature vector for each cell and match cells. 

Results

1. Result of original image sequence.

2. Result of tracking all cells.

3. Result of tracking specific cell in mitosis.

4. Plot of the previous tracking.