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Tracking Colliding Cells ,[object Object],Nhat ‘Rich’ Nguyen,[object Object],Future Computing Lab,[object Object]
Flu,[object Object],Health Center,[object Object],Blood Test,[object Object]
White Count is a blood test to measure the number of white blood cells.,[object Object]
In a drop of blood…,[object Object],Number of ,[object Object],white cells,[object Object],blood cancer,[object Object],50,000,[object Object],stress, viral infection, drug intake,[object Object],25,000,[object Object],healthy,[object Object],5,000,[object Object],flu, poisoning,[object Object],0,[object Object]
It isimportant to keep track of white blood cells.,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Video of white blood cells via a microscope,[object Object]
Tracking Colliding Cells
Manual,[object Object],Automatic,[object Object],Tedious,[object Object],Expensive,[object Object],Subjective,[object Object],Little Effort,[object Object],Economical,[object Object],Objective,[object Object]
As many cells move at a wide range of speeds…,[object Object],Collisions,[object Object]
abrupt,[object Object],change,[object Object]
Smoothness Constraints,[object Object],Region A,[object Object],broken tracks,[object Object],Region A,[object Object],robust tracks,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Smoothness Constraint,[object Object],Region A,[object Object],broken tracks,[object Object],Region A,[object Object],Region A,[object Object],robust tracks,[object Object],Our Method,[object Object]
The first tracking method for colliding cells.,[object Object]
Training,[object Object],100 cell samples,[object Object],100background samples,[object Object]
Detection ,[object Object],Classify each pixel as a Cell or Background,[object Object]
Tracking,[object Object],time,[object Object]
Kalman Filter,[object Object],Popular,[object Object],Extensively used for tracking.,[object Object],Optimal,[object Object],Estimate the most probable state.,[object Object],Simple,[object Object],Two steps: predict and correct.,[object Object]
No Collision,[object Object],Collision,[object Object],smooth,[object Object],smooth & abrupt,[object Object],reliability,[object Object],flexibility,[object Object],?,[object Object],Kalman filter,[object Object]
Multiple Hypotheses,[object Object],H2,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],H1,[object Object],H3,[object Object],H4,[object Object]
No Collision,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],H1,[object Object]
Collision,[object Object],H2,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object]
During Collision,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],H3,[object Object]
After Collision,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],H4,[object Object]
H2,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],H1,[object Object],H3,[object Object],H4,[object Object]
The reliability of the Kalman filter, the flexibility of multiple hypotheses.,[object Object]
Tracking Steps,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],2,[object Object],3,[object Object]
1,[object Object],colliding cells,[object Object],2,[object Object],3,[object Object],non-colliding cell,[object Object]
stay colliding,[object Object],1,[object Object],2,[object Object],3,[object Object],split away,[object Object],keep moving,[object Object]
Region B,[object Object],Our method,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Data,[object Object],8,[object Object],300,[object Object],~6K,[object Object],image sequences,[object Object],cells tracks ,[object Object],cell positions,[object Object]
112,[object Object],188,[object Object],colliding cells,[object Object],non-colliding cells,[object Object]
Compared Methods,[object Object],SC ,[object Object],Smoothness Constraints ,[object Object],Single Hypothesis ,[object Object],Multiple Hypotheses ,[object Object],SH,[object Object],MH ,[object Object]
Comparisons,[object Object],MH,[object Object],SC,[object Object],SH,[object Object]
Percentage of Tracked Positions,[object Object],MH,[object Object],SH,[object Object],SC,[object Object]
Colliding vs. Non-colliding,[object Object],MH,[object Object],SH,[object Object],SC,[object Object]
Impact of detection ,[object Object],MH ,[object Object],SH,[object Object],SC,[object Object]
Given adequate detection results, our method covers 88% of colliding cell positions.,[object Object]
Challenges,[object Object],Methods,[object Object],Experiments,[object Object]
Conclusion,[object Object],The first tracking method for colliding cells.,[object Object],The reliability of the Kalman filter, the flexibility of multiple hypotheses.,[object Object],Excellent cell positions coverage.,[object Object],Non- ,[object Object],Colliding,[object Object],Colliding,[object Object],88%,[object Object]
Thank you.,[object Object]
Questions ?,[object Object]
Tracking Colliding Cells
Bonus Slides,[object Object]
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.,[object Object],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.,[object Object],[to be submitted to IEEE Transactions on Medical Imaging],[object Object],N. Nguyen, S. Keller, Eric Norris, T. Huynh, M. Shin. “Tracking Colliding Cells in In-Vivo Intravital Microscopy Images”. ,[object Object],Publications,[object Object]
Example of Multiple cell tracking,[object Object]
Tracking Colliding Cells
White Blood Cells,[object Object],Circulate in your blood,[object Object],Defend you against bacteria,[object Object],Protect you from disease,[object Object]
Tracking Colliding Cells
Previous Automatic Methods,[object Object],Ray et al. [2002],[object Object],Active Contour,[object Object],Cui et al. [2005],[object Object],Monte Carlo,[object Object],Mukherjee et al. [2004],[object Object],Level Set Analysis,[object Object]
Previous Automatic Methods,[object Object],Eden et al. [2005],[object Object],SmoothnessConstraints,[object Object],Li et al. [2005],[object Object],Lineage Construction,[object Object],Smith et al. [2008],[object Object],Probabilistic Formalization,[object Object]
Variation in cell appearances within an image,[object Object],time,[object Object],Varied appearance of a cell over time,[object Object]
Qualitative Comparison,[object Object]
Challenges,[object Object],In a collision, cell motion and appearance,[object Object],1. could be different,[object Object],2. change abruptly,[object Object]
Approach,[object Object], To improve tracking accuracy of colliding cells by:,[object Object],having separate collision states,[object Object],to describe cells inside and outside of collisions,[object Object],testing multiple hypotheses,[object Object],of cell motion and appearance as transitions between abrupt motion patterns.,[object Object]
AdaBoost,[object Object],Idea: combine many “rules of thumb” to a highly accurate prediction rule.,[object Object],Input: visual features from training samples.,[object Object],Schema: maintain a strategy to determine “rules of thumb” using weight distribution.,[object Object],Output: a single strong classifier which is a linear combination of the set of weak classifiers.,[object Object]
Detection Procedure ,[object Object],Scan each pixel p in the image,[object Object],Compute image feature vector V from a  window centered around p,[object Object],Classify p as a Cell pixel if the feature score in V  satisfies the learned decision rule; otherwise classify p as a Background pixel.,[object Object],Cluster groups of Cell pixels into cell observation.,[object Object]
Motion and Appearance Model,[object Object],Collision States:,[object Object],State Transition:,[object Object],Hypotheses:,[object Object],to predict the state in the next frame,[object Object],control input vector,[object Object],State Vector*:,[object Object],Motion and Appearance Models: ,[object Object],(for        ),[object Object],state transition,[object Object],matrix,[object Object],control input matrix,[object Object],process noise vector  ~N(0,Qs),[object Object],Observation Vector:,[object Object]
Multiple Hypotheses,[object Object],No ,[object Object],Collision,[object Object],Collision,[object Object]
Performance RMSE,[object Object],RMSE : Root mean squared errors of position (pixel),[object Object],-0.03,[object Object],-0.17,[object Object],+0.36,[object Object],-0.21,[object Object],+0.33,[object Object],-0.20,[object Object],SH introduces additional error in positions.,[object Object],MH does not introduce any additional error.,[object Object],Estimating colliding cells’ positions is more difficult.,[object Object]
Collision Duration RMSE ,[object Object], The effect of collision duration on RMSE,[object Object]
Impact Detection RMSE ,[object Object], The impact of detection on RMSE,[object Object],-0.17,[object Object],-0.13,[object Object],-1.05,[object Object],-1.09,[object Object],Different improvement between dataset.,[object Object],Different improvement between methods.,[object Object]
Future Work,[object Object],1. Add more features to improve detection.,[object Object],5,[object Object],7,[object Object],6,[object Object],8,[object Object]
Future Work,[object Object],2. Incorporate a probabilistic approach to transition between collision states.,[object Object],72,[object Object],73,[object Object],75,[object Object],76,[object Object]
Future Work,[object Object],3. Expand to track cells with more complex motions and behaviors.,[object Object],49,[object Object],50,[object Object],51,[object Object],52,[object Object]
Detection Performance,[object Object],Recall :  TP / (TP + FN),[object Object],75%,[object Object],Precision: TP / (TP + FP),[object Object],77%,[object Object]
Collision Duration,[object Object], The effect of collision duration on tracking,[object Object],6,[object Object],112,[object Object],Exclude SC from being considered for collision.,[object Object],Classify colliding positions into bins based on the number of frames of the collision.,[object Object],colliding cells,[object Object],bins  of,[object Object],collision duration,[object Object]
Detection Impact ,[object Object], The impact of detection on tracking,[object Object],38,[object Object],596,[object Object],Data with good detection results before and after collision (+/- 2 frames),[object Object],cell positions,[object Object],treated,[object Object],colliding cells,[object Object]
Performance Table,[object Object],PTP: Percentage of Tracked Positions (%),[object Object],+27,[object Object],+9,[object Object],+23,[object Object],+3,[object Object],+4,[object Object],+24,[object Object],+28,[object Object]
Detection Impact Table,[object Object],+9,[object Object],+7,[object Object],+16,[object Object],+18,[object Object]
More Results,[object Object],Eden et al. [2005],[object Object],Our Method,[object Object]
Tracking Steps,[object Object],Predict motion,[object Object],Predict collision,[object Object],Get measurements,[object Object],Get errors in position & area ,[object Object],Match with minimal error,[object Object]
Collision States:,[object Object],Hypotheses:,[object Object],to predict the state in the next frame,[object Object],control input vector,[object Object],State Vector*:,[object Object],Motion and Appearance Models: ,[object Object],(for        ),[object Object],state transition,[object Object],matrix,[object Object],control input matrix,[object Object],process noise vector  ~N(0,Qs),[object Object],Observation Vector:,[object Object],Measurement Model:,[object Object],measurement noise vector ~N(0,R),[object Object],measurement ,[object Object],transition matrix,[object Object]
State Vector of cell i :,[object Object],Predicted State Vector:,[object Object],Zero Matrix,[object Object],Zero Matrix,[object Object],Zero Vector,[object Object],Zero Vector,[object Object],Predicted Covariance:,[object Object]
Predicted State Vector:,[object Object],Hypothesized Measurement Vector:,[object Object],measurement ,[object Object],transition matrix,[object Object],Error of hypothesis       :,[object Object],observation from ,[object Object],the detector,[object Object],weight vector,[object Object],Rule 1:,[object Object],Rule 2:,[object Object],error threshold of ,[object Object],Unlikely (i, k) pair,[object Object],Stop corresponding condition:,[object Object]
Remaining Observation          :,[object Object],new cell,[object Object],leukocyte typical,[object Object],diameter,[object Object],area,[object Object],Remaining Cell       :,[object Object],Not corresponded for 3 frames:,[object Object],Updated State Vector :,[object Object],Kalman gain,[object Object],Updated Covariance:,[object Object],depends on the cell ,[object Object],current state s ,[object Object],[object Object],abrupt change in collision,[object Object]
Training,[object Object],100 Cell Samples,[object Object],100 Background Samples,[object Object],Features,[object Object],Mean Intensity,[object Object],Standard Dev. of Intensity,[object Object],Radial Mean,[object Object], Decision Rules on feature scores,[object Object]
Collision Duration,[object Object],The effect of collision duration on PTP,[object Object]
H01,[object Object],H00,[object Object],No ,[object Object],Collision,[object Object],(s = 0),[object Object],Collision,[object Object],(s = 1),[object Object],H11,[object Object],H10,[object Object]
Measurements,[object Object],Cell matches,[object Object],Cell Detection,[object Object],Correspondence,[object Object],Update,[object Object],Cell,[object Object],image,[object Object],Finished,[object Object],tracks,[object Object],Tracks,[object Object],Predictions,[object Object],Multiple Hypotheses,[object Object],H00:,[object Object],No Collision – No Collision,[object Object],H01:,[object Object],No Collision –Collision,[object Object],H11:,[object Object],Collision –Collision,[object Object],H10:,[object Object],Collision –   ,[object Object],No Collision,[object Object]

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Tracking Colliding Cells

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Hinweis der Redaktion

  1. Motion model