Wbc master thesisdefense

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  • Wbc master thesisdefense

    1. 1. Tracking Colliding Cells <br />Nhat ‘Rich’ Nguyen<br />Committee Members: Min Shin, PhD, <br />Richard Souvenir, PhD, and Toan Huynh, MD<br />
    2. 2. White Blood Cells<br />“The little warriors of your body”<br /><ul><li>Protect us against infection
    3. 3. Lookout for signs of disease</li></ul>Microscopic scale: 7,000 to 25,000 in a drop of blood<br />2<br />
    4. 4. Flowing<br />Rolling<br />Adhering<br /> Motion Behaviors<br />3<br />Microscopy Video of white blood cells<br />
    5. 5. Biologists learn cell behaviors by tracking a few cells<br />4<br />
    6. 6. Manual<br />Automatic<br />Tedious<br />Little Effort<br />Expensive<br />Inexpensive<br />Subjective<br />Objective<br />5<br />
    7. 7. Previous Automatic Methods<br />Ray et al. [2002]<br />Active Contour<br />Cui et al. [2005]<br />Monte Carlo<br />Mukherjee et al. [2004]<br />Level Set Analysis<br />6<br />
    8. 8. Previous Automatic Methods<br />Eden et al. [2005]<br />SmoothnessConstraints<br />Li et al. [2005]<br />Lineage Construction<br />Smith et al. [2008]<br />Probabilistic Formalization<br />7<br />
    9. 9. Qualitative Comparison<br />8<br />
    10. 10. Challenges<br />As many cells move at <br />a wide range of speed<br />Collisions!<br />9<br />
    11. 11. Challenges<br />In a collision, cell motion and appearance<br />1. could be different<br />2. change abruptly<br />10<br />
    12. 12. Example <br />Eden et al. [2005]<br />broken tracks<br />robust tracks<br />Our Method<br />11<br />
    13. 13. Approach<br /> To improve tracking accuracy of colliding cells by:<br />having separate collision states<br />to describe cells inside and outside of collisions<br />testing multiple hypotheses<br />of cell motion and appearance as transitions between abrupt motion patterns.<br />12<br />
    14. 14. Cell Detection <br />Classify each pixel in the image as a Cell or Background<br />13<br />
    15. 15. Detection Problems<br />14<br />Variation in cell appearances within an image<br />time<br />Varied appearance of a cell over time<br />
    16. 16. AdaBoost<br />15<br />Idea: combine many “rules of thumb” to a highly accurate prediction rule.<br />Input: visual features from training samples.<br />Schema: maintain a strategy to determine “rules of thumb” using weight distribution.<br />Output: a single strong classifier which is a linear combination of the set of weak classifiers.<br />
    17. 17. Training<br />100 Cell Samples<br />100 Background Samples<br />Features<br />Mean Intensity<br />Standard Dev. of Intensity<br />Radial Mean<br /> Decision Rules on feature scores<br />16<br />
    18. 18. Detection Procedure <br />Scan each pixel p in the image<br />Compute image feature vector V from a window centered around p<br />Classify p as a Cell pixel if the feature score in V satisfies the learned decision rule; otherwise classify p as a Background pixel.<br />Cluster groups of Cell pixels into cell observation.<br />17<br />
    19. 19. Cell Tracking<br />1. Predict<br />using multiple hypotheses<br />2. Correspond<br />predictions and measurements<br />3. Update<br />based on the current state<br />18<br />
    20. 20. Kalman Filter<br />19<br />Extensively used for tracking.<br />Estimator of the state that is optimal.<br />Consist of two steps:<br /><ul><li>Predict: projects next time step state.
    21. 21. Correct: incorporates a new measurement into a priori estimate to obtain an improved posteriori estimate.</li></li></ul><li>smooth<br />abrupt<br />?<br />Kalman<br />H00: No collision – No collision<br />H11: Collision – Collision<br />H01: No collision – Collision<br />H10: Collision – No collision<br />Collision States<br />20<br />
    22. 22. Motion and Appearance Model<br />21<br />Collision States:<br />State Transition:<br />Hypotheses:<br />to predict the state in the next frame<br />control input vector<br />State Vector*:<br />Motion and Appearance Models: <br />(for )<br />state transition<br />matrix<br />control input matrix<br />process noise vector ~N(0,Qs)<br />Observation Vector:<br />
    23. 23. 22<br />
    24. 24. 23<br />Tracking Steps<br />3<br />1<br />2<br />4<br />
    25. 25. 24<br />Tracking Steps<br />3<br />1<br />2<br />4<br />
    26. 26. 25<br />Tracking Steps<br />3<br />1<br />2<br />4<br />
    27. 27. 26<br />Tracking Steps<br />3<br />1<br />2<br />4<br />
    28. 28. 27<br />Tracking Steps<br />3<br />1<br />2<br />4<br />
    29. 29. 28<br />Tracking Steps<br />3<br />1<br />correspond to H01<br />no measurement<br />2<br />4<br />correspond to H00<br />no prediction<br />
    30. 30. 29<br />Tracking Steps<br />3<br />1<br />colliding cells<br />lost cell<br />2<br />4<br />5<br />non-colliding cell<br />new cell<br />
    31. 31. 30<br />Tracking Steps<br />3<br />keep moving<br />stay colliding<br />1<br />4<br />split away<br />2<br />5<br />keep moving<br />start moving<br />
    32. 32. 31<br />
    33. 33. Data<br />8<br />300<br />~6k<br />image sequences<br />cells tracks <br />cell positions<br />32<br />
    34. 34. Colliding vs. Non-colliding<br />33<br />112<br />188<br />colliding cells<br />non-colliding cells<br />
    35. 35. Evaluating Methods<br />34<br />Smoothness Constraints (SC)<br />Single Hypothesis (SH)<br />Multiple Hypotheses (MH)<br />
    36. 36. Detection Performance<br />35<br />Recall : TP / (TP + FN)<br />75%<br />Precision: TP / (TP + FP)<br />77%<br />
    37. 37. Tracking Performance<br />36<br />RMSE : Root mean squared errors of position (pixel)<br />-0.03<br />-0.17<br />+0.36<br />-0.21<br />+0.33<br />-0.20<br />SH introduces additional error in positions.<br />MH does not introduce any additional error.<br />Estimating colliding cells’ positions is more difficult.<br />
    38. 38. Tracking Performance<br />37<br />PTP: Percentage of Tracked Positions (%)<br />+27<br />+9<br />+23<br />+3<br />+4<br />+24<br />+28<br />Large improvement in colliding positions.<br />Improvement overall.<br />Tracking colliding cells is more difficult.<br />
    39. 39. Comparisons<br />MH <br />SC <br />SH<br />38<br />
    40. 40. Discussions <br />39<br /> The effect of collision duration on tracking<br />6<br />112<br />Exclude SC from being considered for collision.<br />Classify colliding positions into bins based on the number of frames of the collision.<br />colliding cells<br />bins of<br />collision duration<br />
    41. 41. Discussions <br />40<br /> The effect of collision duration on RMSE<br />
    42. 42. Discussions <br />41<br /> The effect of collision duration on PTP<br />
    43. 43. Discussions <br />42<br /> The impact of detection on tracking<br />38<br />596<br />Data with good detection results before and after collision (+/- 2 frames)<br />cell positions<br />treated<br />colliding cells<br />
    44. 44. Discussions <br />43<br /> The impact of detection on RMSE<br />-0.17<br />-0.13<br />-1.05<br />-1.09<br />Different improvement between dataset.<br />Different improvement between methods.<br />
    45. 45. Discussions <br />44<br /> The impact of detection on PTP<br />+9<br />+7<br />+16<br />+18<br />Large improvement between dataset.<br />Large different between methods.<br />MH achieves high performance in tracking.<br />
    46. 46. Results<br />Region B<br />Our Method<br />45<br />
    47. 47. Future Work<br />1. Add more features to improve detection.<br />46<br />5<br />7<br />6<br />8<br />
    48. 48. Future Work<br />2. Incorporate a probabilistic approach to transition between collision states.<br />47<br />72<br />73<br />75<br />76<br />
    49. 49. Future Work<br />3. Expand to track cells with more complex motions and behaviors.<br />48<br />49<br />50<br />51<br />52<br />
    50. 50. Conclusion<br />49<br />A method for tracking colliding cells.<br />Incorporate Kalman filter and multiple hypotheses for each collision state.<br />Improve 28%in tracked position coverage compared to a previous work .<br />Achieve 88% in tracked position coverage in tracking colliding cells.<br />
    51. 51. Questions/Comments<br />50<br />
    52. 52. S. J. Schmugge, S. Keller, N. Nguyen, R. Souvenir, T. H. Huynh, M. Clemens, M. C. Shin. "Segmentation of Vessels Cluttered with Cells using a Physics based Model". 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), New York, September 6-10 2008.<br />N. Nguyen, S. Keller, T. Huynh, M. Shin. “Tracking Colliding Cells”. IEEE Workshop on Applications of Computer Vision (WACV), Snowbird, UT December 07-09 2009.<br />to be submitted to IEEE Transactions on Medical Imaging<br />N. Nguyen, S. Keller, Eric Norris, T. Huynh, M. Shin. “Tracking Colliding Cells in Intravital Microscopy Images”. <br />Related Publications<br />51<br />
    53. 53. More Results<br />52<br />Eden et al. [2005]<br />Our Method<br />
    54. 54. 53<br />Tracking Steps<br />Predict motion<br />Predict collision<br />Get measurements<br />Get errors in position & area <br />Match with minimal error<br />
    55. 55. Collision States:<br />Hypotheses:<br />to predict the state in the next frame<br />control input vector<br />State Vector*:<br />Motion and Appearance Models: <br />(for )<br />state transition<br />matrix<br />control input matrix<br />process noise vector ~N(0,Qs)<br />Observation Vector:<br />Measurement Model:<br />measurement noise vector ~N(0,R)<br />measurement <br />transition matrix<br />54<br />
    56. 56. State Vector of cell i :<br />Predicted State Vector:<br />Zero Matrix<br />Zero Matrix<br />Zero Vector<br />Zero Vector<br />Predicted Covariance:<br />55<br />
    57. 57. z1<br />x’1a<br />Calculate error between all possible pairs <br />Sm & On<br />Criteria: Position & Area.<br />Assign pairs via Greedy Search<br />HФ<br />2. Correspondence<br />X1<br />x’1b<br />HФ<br />Cell Detector<br />z2<br />x'2a<br />HФ<br />X2<br />HФ<br />x’2b<br />z3<br />x’3a<br />HФ<br />X3<br />HФ<br />x’3b<br />zn<br />X1<br />x’ka<br />HФ<br />z1<br />x’1a<br />Xk<br />HФ<br />x’kb<br />X2<br />x’2b<br />z3<br />Cell 3 is missing<br />x’3a<br />X3<br />New cell has entered<br />z2<br />X4<br />Ck<br />zn<br />x’kb<br />56<br />
    58. 58. Predicted State Vector:<br />Hypothesized Measurement Vector:<br />measurement <br />transition matrix<br />Error of hypothesis :<br />observation from <br />the detector<br />weight vector<br />Rule 1:<br />Rule 2:<br />error threshold of <br />Unlikely (i, k) pair<br />Stop corresponding condition:<br />57<br />
    59. 59. Remaining Observation :<br />new cell<br />leukocyte typical<br />diameter<br />area<br />Remaining Cell :<br />Not corresponded for 3 frames:<br />Updated State Vector :<br />Kalman gain<br />Updated Covariance:<br />depends on the cell <br />current state s <br /><ul><li>Eliminate the affect cause by </li></ul>abrupt change in collision<br />58<br />
    60. 60. 59<br />II. AdaBoost1. Adaptive Boosting method<br />Robert Schapire algorithm (1996).<br />Idea:<br />Combine many “rules of thumb”  a highly accurate prediction rule.<br />Maintain a strategy to determine “rules of thumb”  weight.<br />Terms:<br />Learner = Hypothesis = Classifier.<br />Weak Learner: <50% error rate.<br />Strong Learner: linear combination of weak learner.<br />
    61. 61. 60<br />II. AdaBoost2. An Example of Boosting<br />Plastic Apple<br />Real Apple<br />
    62. 62. 61<br />II. AdaBoost3. Features Space<br />Weight<br />Color<br />
    63. 63. 62<br />II. AdaBoost4. 1st Iteration<br />Wrong guess<br />Wrong<br />guess<br />Weak Learner <br />Optimal Guessing line<br />
    64. 64. 63<br />II. AdaBoost5. 1st Reweight<br />Wrong guess<br />Gets larger<br />Get smaller<br />Right guess<br />Gets smaller<br />
    65. 65. 64<br />II. AdaBoost6. 2nd Iteration<br />More weighted ones<br />get classify correctly<br />Wrong guess<br />Wrong guess<br />
    66. 66. 65<br />II. AdaBoost7. 2nd Reweight<br />smaller<br />Wrong guess<br />Gets larger<br />smaller<br />Right guess<br />Gets smaller<br />
    67. 67. 66<br />II. AdaBoost8. 3rd Iteration<br />
    68. 68. 67<br />II. AdaBoost9. 4th Iteration<br />
    69. 69. 68<br />II. AdaBoost10. Combination<br />3rd<br />2nd<br />1st<br />4th<br />
    70. 70. 69<br />II. AdaBoost11. Decision<br />Plastic Apple<br />Region<br />Decision<br />Boundary<br />Real Apple<br />Region<br />
    71. 71. 70<br />Tracking Steps<br />
    72. 72. 71<br />Tracking Steps<br />
    73. 73. 72<br />
    74. 74. 73<br />
    75. 75. Tracking Performance<br />74<br />+.24<br />+.04<br />+.23<br />+.03<br />+.23<br />+.09<br />+.21<br />+.18<br />
    76. 76. Discussions <br />75<br /> The impact of detection on PTP<br />+7<br />+16<br />+11<br />Large improvement between dataset.<br />Large different between methods.<br />MH achieves high performance in tracking.<br />
    77. 77. Training<br />100 Cell Samples<br />100 Background Samples<br />Features<br />Mean Intensity<br />Standard Dev. of Intensity<br />Radial Mean<br /> Decision Rules on feature scores<br />76<br />
    78. 78. 77<br />

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