Tracking Cells in DIC Images

I am working on under agarose assays, in which I take time lapse videos of cells moving on a plastic surface underneath agarose gel. While I have attempted to use fluorescent markers to label the cells and make them easier to identify, it never worked out well and the best images I have obtained are through DIC live cell microscopy. I know from reading the forum that Cell Profiler does not handle DIC images too well and I have tried to incorporate the ideas you have provided on dealing with these images, but I am still having trouble.

The pipeline I have currently is as follows. The goal is to identify the cells, track them through the entire set of time lapse images and obtain a video of the tracked cells moving as well as measurements on their total distance moved:

File type to be loaded:individual images
File selection method:Text-Exact match

Select the input image:Orig
Name the output image:Cropped
Select the cropping shape:Rectangle
Select the cropping method:Mouse
Apply which cycle’s cropping pattern?:First

Select the input image:Cropped
Name the output image:Edged
Automatically calculate the threshold?:Yes
Absolute threshold:0.2
Threshold adjustment factor:1
Select an edge-finding method:Canny
Select edge direction to enhance:All
Calculate Gaussian’s sigma automatically?:Yes
Gaussian’s sigma value:10
Calculate value for low threshold automatically?:Yes
Low threshold value:0.1

Select the input image:Edged
Name the primary objects to be identified:Cells
Typical diameter of objects, in pixel units (Min,Max):30,100
Discard objects outside the diameter range?:Yes
Try to merge too small objects with nearby larger objects?:Yes
Discard objects touching the border of the image?:No
Select the thresholding method:Otsu Adaptive
Threshold correction factor:0.1
Lower and upper bounds on threshold:0,1.0
Approximate fraction of image covered by objects?:0.01
Method to distinguish clumped objects:Intensity
Method to draw dividing lines between clumped objects:Intensity
Size of smoothing filter:10
Suppress local maxima that are closer than this minimum allowed distance:5
Speed up by using lower-resolution image to find local maxima?:Yes
Name the outline image:CellOutlines
Fill holes in identified objects?:Yes
Automatically calculate size of smoothing filter?:Yes
Automatically calculate minimum allowed distance between local maxima?:Yes

Choose a tracking method:Distance
Select the objects to track:Cells
Select object measurement to use for tracking:Intensity_Area
Maximum pixel distance to consider matches:50
Select display option:Color and Number
Save color-coded image?:Yes
Name the output image:TrackedCells


Select the type of image to save:Movie
Select the image to save:TrackedCells

While the pipeline seems to work fairly well, it is not identifying the cells reliably and doesn’t always do a good job tracking them from image to image. I have attached an example picture of the cells I am trying to identify and track. Any suggestions you have for me would be much appreciated, whether on the pipeline itself or on any ways I could format the images prior to loading them into Cell Profiler.


Hi Marshall,

Based on the image you have uploaded, I have difficulty seeing how CellProfiler (or any other biological image processing software) would be able handle this image. Unfortunately, there is almost no cell-to-cell or cell-to-background contrast to speak, and I would have a hard time distinguishing cells by eye. If a human cannot make out the features of interest, it is most likely not possible for a computer to automatically do the same either.


Hi Mark,

I realize that the image I provided is not the best resolution and I am working on getting better ones through further experiments. Do you have any suggestions on capturing more clear and distinct DIC images or any thoughts on how I could improve the pipeline I already have?

Would you mind posting the pipeline itself to the forum? I know that you placed the text in-line in your initial post, but it’s best to have the original just so nothing gets lost in the translation.

Sure. Here it is…

Cell Tracker.cp (8.32 KB)

After looking at the pipeline (and also a colleague who is an expert in ImageJ), I don’t think these images are tractable for analysis without either using a fluorescence-based solution, or teaming up with a computer scientist who wouldn’t mind taking this on as a project.