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Detection of Cell Mitosis for Population Growth Analysis

Detection of Cell Mitosis for Population Growth Analysis. Kyri Baker Carnegie Mellon University Methods in Medical Image Analysis Spring 2010. Outline. Objective/goal of this project Motivation Steps to achieve this goal and difficulties encountered Results and other methods Questions.

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Detection of Cell Mitosis for Population Growth Analysis

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  1. Detection of Cell Mitosis for Population Growth Analysis Kyri Baker Carnegie Mellon University Methods in Medical Image Analysis Spring 2010 Kyri Baker 2010

  2. Outline • Objective/goal of this project • Motivation • Steps to achieve this goal and difficulties encountered • Results and other methods • Questions Kyri Baker 2010

  3. Objective • Detect stem cell mitosis (divisions) • Automatically monitor how cell population changes over time when injected with a growth hormone Mitotic Event: Cell brightens, enlarges, and divides Kyri Baker 2010

  4. Motivation • Want to farm stem cells for use in tissue regeneration • Biologists culture groups of cells and monitor how they react to specific growth hormones • A sign of a good growth hormone is one that causes the lineage tree to be symmetric Mitosis Cell Lineage Tree Mitosis Kyri Baker 2010

  5. The Data • Hematopoietic stem cells from the bone marrow of adults • Dosed with a growth drug and chemicals to prevent clumping • Imaged every 30 seconds ≈ 11,000 frames Frame 1 Frame 11,000 Kyri Baker 2010

  6. How to Detect Mitosis? • Many techniques can be experimented with - In general, cells become brighter and rounder - But illumination conditions can change, other cells can also be round • For certain: Cell area increases because another cell is being “born”!  Meat of the algorithm: Monitor overall cell areas over time, and when the area of a cell starts to increase, mitosis is happening  Challenge: How to accurately find the cell areas? Kyri Baker 2010

  7. Step 1: Preprocess Image 1. Import Image 2. Threshold Image 3. Apply Hough Transform for circle detection 4. Set all pixels outside detected circle to black Kyri Baker 2010

  8. Step 2: Correlation Filter • To refine the boundaries of cell nucleus and cell membrane • Used itkNormalizedCorrelationImageFilterwith an annulus mask • Cannot correlate with a template like: - Cells touching looks like mitosis: Mitosis Frames after Correlation Filter Kyri Baker 2010

  9. Step 3: Watershed Image Filter • Find the areas of different cells Mitotic Cell Non-Mitotic Cells Larger Area Mitotic Cell Difficulties:False positives from image noise Nucleus Area ≈ 90-120 Nucleus Area ≈ 60-90 Kyri Baker 2010

  10. Mini Steps: Relabeler, ColorFilter • Order watershed areas from largest to smallest with itk::RelabelComponentImageFilter • For nice watershed output visualization used itk::Functor::ScalarToRGBPixelFunctor Kyri Baker 2010

  11. Results Actual Mitosis Algorithm Detected Mitosis Number of Mitotic Cells Frame • Current best results were achieved with: • Lower intensity threshold = 120 • Taking watershed regions with area > 120 pixels • Only keep as a true “mitotic event” if mitosis was detected over a series of 3 frames • or more Kyri Baker 2010

  12. Other Methods Attempted • Subtracting previous frame to determine cell growth • Cells moved around too much, too many false positives • Monitoring overall summed area of all cells • Overall area calculation fluctuated too greatly, cannot determine exact instant of mitosis Overall Cell Area Frame Kyri Baker 2010

  13. Questions? Kyri Baker 2010

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