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Automatic in vivo Microscopy Video Mining for Leukocytes

Automatic in vivo Microscopy Video Mining for Leukocytes. * Chengcui Zhang, Wei-Bang Chen, Lin Yang, Xin Chen, John K. Johnstone. Background Information. What is in vivo microscopy? Images of the cellular and molecular processes in a living organism Why video-mine leukocytes?

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Automatic in vivo Microscopy Video Mining for Leukocytes

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  1. Automatic in vivo Microscopy Video Mining for Leukocytes * Chengcui Zhang, Wei-Bang Chen, Lin Yang, Xin Chen, John K. Johnstone

  2. Background Information • What is in vivo microscopy? • Images of the cellular and molecular processes in a living organism • Why video-mine leukocytes? • To Predict Inflammatory response • Rolling velocity and magnitude of adhesion of leukocytes are the main predictors • Currently analyzed manually • Time consuming / Expensive • Subjective

  3. Objectives • Given a sequence of in vivo images, • Track the moving leukocytes • Calculate their average velocity • Find the magnitude of adherent leukocytes

  4. Challenges • Server Noise • Background movement • Due to movement of the living organism • Deformation of leukocytes • Change of contrast in different frames

  5. Previous Work • [Eden et al.] use local features (e.g. color) for a tracking system • Assume that leukocytes roll along the vessel centerline • [Acton et al.] Background removal + morphological filter • Assumes the shape/size leukocytes does not change

  6. Suggested Approach • Three main steps: • Frame Alignment • To correct the camera/subject movement • Detect Moving Leukocytes • Detect Adherent Leukocytes • After moving leukocytes are removed

  7. Step 1- Frame Alignment • 1.1- Detect Camera/Subject Movement • Define a (dis)similarity measure between consecutive frames • This allows for some tolerance within radius r • If S(ft-1, ft) is larger than a threshold, then ft requires frame alignment

  8. Step 1- Frame Alignment • 1.2- Frame Matching • Generate a number of high dimensional, local scale-invariant features [SIFT] for the frame and its predecessor • Use nearest-neighbor to find a match for each feature point • Calculate the transformation matrix H, such that • For every matched point x and x’

  9. Step 1- Frame Alignment • Use Random Sample Consensus (RANSAC) to correct the mismatches

  10. Step 2 - Detecting Moving Leukocytes • Approach 1 - Probabilistic Learning • For pixel j in the image, let x1j, x2j, ..., xNj be the intensity of the pixel over N frames • Assume that P(xtj) has a normal distribution over time with mean xtj • If P(xtj) is smaller than a threshold, then it is a foreground pixel • Problem: Difficult to find a threshold

  11. Step 2 - Detecting Moving Leukocytes • Approach 1 - Probabilistic Learning • Problem: Difficult to find the threshold value • Solution: Use One-Class SVM to classify background and foreground pixels

  12. Step 2 - Detecting Moving Leukocytes • Approach 2 - Neural Network • Train a neural net to learn the predictable pattern of the background pixels • Input: [x(t-m), x(t-m+1),... , x(t-1)] • A sliding window of the intensity sequence • Output: x(t) • Prediction for the intensity of the pixel at the next frame • If the neural-net prediction and the real pixel intensity are very different, the pixel in the current frame is in foreground

  13. Step 2 - Detecting Moving Leukocytes • Approach 2 - Neural Network

  14. Step 2 - Detecting Moving Leukocytes • Calculating the leukocytes velocity • Find the centroid of each group of connected foreground pixels • For each centroid, find the closest centroid in the previous frame • If their distance is smaller than a threshold, they are a match • Compute the mean velocity

  15. Step 3- Detecting Adherent Leukocytes • First, remove the moving leukocytes • Three main types of regions left • Tissues • Vessels • Adherent Leukocytes • These three have different intensity values

  16. Step 3- Detecting Adherent Leukocytes

  17. Step 3- Detecting Adherent Leukocytes • Finding the threshold values • Fit an 8th degree polynomial to the histogram curve • The real part of the second largest root is the ideal threshold • Justification? • Problem with false positives and false negatives

  18. Experimental Results • Test video of 148 frames • Detecting moving leukocytes: • 1% false positive for probabilistic learning(?) • 49% false positive for neural-net approach • 50% recall • Detecting Adherent leukocytes • 2% false positive • 95% recall

  19. Final Remarks • Paper is mainly related to Vision • The algorithms require many “magic parameters” that need hand tuning • Would the current parameters work as well for a new video sequence from a new equipment? • Do we want to pursue more video-mining papers?

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